Methods and systems for indicating behavior in a population cohort

ABSTRACT

Avatars, methods, apparatuses, computer program products, devices and systems are described that carry out identifying a member of a population cohort; and indicating at least one behavior in the member of the population cohort based on an association between the population cohort and at least one cohort-linked avatar.

INCORPORATION-BY-REFERENCE TO OTHER APPLICATIONS

All subject matter of the Incorporated-by-reference Applications and ofany and all parent, grandparent, great-grandparent, etc. applications ofthe Incorporated Applications is incorporated herein by reference to theextent such subject matter is not inconsistent herewith.

INCORPORATED-BY-REFERENCE APPLICATIONS

U.S. patent application Ser. No. 12/002,289, entitled METHODS ANDSYSTEMS FOR SPECIFYING AN AVATAR, naming Edward K. Y. Jung, Eric C.Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., and Lowell L. Wood, Jr. as inventors, filed 13 Dec. 2007.

U.S. patent application Ser. No. 12/002,778, entitled METHODS ANDSYSTEMS FOR IDENTIFYING AN AVATAR-LINKED POPULATION COHORT, namingEdward K. Y. Jung, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord,Mark A. Malamud, John D. Rinaldo, Jr., and Lowell L. Wood, Jr. asinventors, filed 17 Dec. 2007.

U.S. patent application Ser. No. 12,005,115, entitled METHODS ANDSYSTEMS FOR COMPARING MEDIA CONTENT, naming Edward K. Y. Jung, Eric C.Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., and Lowell L. Wood, Jr. as inventors, filed 19 Dec. 2007.

U.S. patent application Ser. No. 12,006,234, entitled METHODS ANDSYSTEMS FOR SPECIFYING AN AVATAR-LINKED POPULATION COHORT, naming EdwardK. Y. Jung, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A.Malamud, John D. Rinaldo, Jr., and Lowell L. Wood, Jr. as inventors,filed Dec. 2007.

U.S. patent application Ser. No. 12,005,114, entitled METHODS ANDSYSTEMS FOR EMPLOYING A COHORT-LINKED AVATAR, naming Edward K. Y. Jung,Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud,John D. Rinaldo, Jr., and Lowell L. Wood, Jr. as inventors, filed Dec.2007.

U.S. patent application Ser. No. 12,005,168, entitled METHODS ANDSYSTEMS FOR DETERMINING INTEREST IN A COHORT-LINKED AVATAR, namingEdward K. Y. Jung, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord,Mark A. Malamud, John D. Rinaldo, Jr., and Lowell L. Wood, Jr. asinventors, filed Dec. 2007.

U.S. patent application Ser. No. 12,005,063, entitled METHODS ANDSYSTEMS FOR SPECIFYING A COHORT-LINKED AVATAR ATTRIBUTE, naming EdwardK. Y. Jung, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A.Malamud, John D. Rinaldo, Jr., and Lowell L. Wood, Jr. as inventors,filed Dec. 2007.

U.S. patent application Ser. No. 12,005,067, entitled METHODS ANDSYSTEMS FOR INDICATING BEHAVIOR IN A POPULATION COHORT, naming Edward K.Y. Jung, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A.Malamud, John D. Rinaldo, Jr., and Lowell L. Wood, Jr. as inventors,filed Dec. 2007.

TECHNICAL FIELD

This description relates to data capture and data handling techniques.

SUMMARY

An embodiment provides a method. In one implementation, the methodincludes but is not limited to identifying a member of a populationcohort; and indicating at least one behavior in the member of thepopulation cohort based on an association between the population cohortand at least one cohort-linked avatar. In addition to the foregoing,other method aspects are described in the claims, drawings, and textforming a part of the present disclosure.

In one or more various aspects, related systems include but are notlimited to circuitry and/or programming for effecting theherein-referenced method aspects; the circuitry and/or programming canbe virtually any combination of hardware, software, and/or firmwareconfigured to effect the herein-referenced method aspects depending uponthe design choices of the system designer.

An embodiment provides a system. In one implementation, the systemincludes but is not limited to circuitry for identifying a member of apopulation cohort; and circuitry for indicating at least one behavior inthe member of the population cohort based on an association between thepopulation cohort and at least one cohort-linked avatar. In addition tothe foregoing, other system aspects are described in the claims,drawings, and text forming a part of the present disclosure.

An embodiment provides a computer program product. In oneimplementation, the computer program product includes but is not limitedto a signal-bearing medium bearing (a) one or more instructions foridentifying a member of a population cohort; and (b) one or moreinstructions for indicating at least one behavior in the member of thepopulation cohort based on an association between the population cohortand at least one cohort-linked avatar. In addition to the foregoing,other computer program product aspects are described in the claims,drawings, and text forming a part of the present disclosure.

An embodiment provides a system. In one implementation, the systemincludes but is not limited to a computing device and instructions. Theinstructions when executed on the computing device cause the computingdevice to (a) identify a member of a population cohort; and (b) indicateat least one behavior in the member of the population cohort based on anassociation between the population cohort and at least one cohort-linkedavatar. In addition to the foregoing, other system aspects are describedin the claims, drawings, and text forming a part of the presentdisclosure.

In one or more various aspects, related systems include but are notlimited to computing means and/or programming for effecting the hereinreferenced method aspects; the computing means and/or programming may bevirtually any combination of hardware, software, and/or firmwareconfigured to effect the herein referenced method aspects depending uponthe design choices of the system designer.

In addition to the foregoing, various other method and/or system and/orprogram product aspects are set forth and described in the teachingssuch as text (e.g., claims and/or detailed description) and/or drawingsof the present disclosure.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is NOT intended to be in any way limiting. Otheraspects, features, and advantages of the devices and/or processes and/orother subject matter described herein will become apparent in theteachings set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

With reference now to FIG. 1, shown is an example of an avatar attributespecification system in which embodiments may be implemented, perhaps ina device and/or through a network, which may serve as a context forintroducing one or more processes and/or devices described herein. Alsodepicted on FIG. 1 is the example operational flow of FIG. 24 describedbelow.

FIG. 2 illustrates certain alternative embodiments of the system of FIG.1.

FIG. 3 illustrates certain alternative embodiments of the system of FIG.1.

FIG. 4 shows diagrammatic views of the surface of the human brain.

With reference now to FIG. 5, shown is an example of an operational flowrepresenting example operations related to specifying an avatarattribute, which may serve as a context for introducing one or moreprocesses and/or devices described herein.

FIG. 6 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 7 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 8 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 9 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 10 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 11 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 12 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 13 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 14 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 15 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 16 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 17 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 18 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

FIG. 19 illustrates an alternative embodiment of the example operationalflow of FIG. 5.

With reference now to FIG. 20, shown is a partial view of an examplecomputer program product that includes a computer program for executinga computer process on a computing device related to specifying an avatarattribute, which may serve as a context for introducing one or moreprocesses and/or devices described herein.

With reference now to FIG. 21, shown is an example device in whichembodiments may be implemented related to specifying an avatarattribute, which may serve as a context for introducing one or moreprocesses and/or devices described herein.

With reference now to FIG. 22, shown is an example of a system forindicating behavior in a population cohort in which embodiments may beimplemented, perhaps in a device and/or through a network, which mayserve as a context for introducing one or more processes and/or devicesdescribed herein. Also depicted on FIG. 22 is the example operationalflow of FIG. 24 described below.

FIG. 23 illustrates certain alternative embodiments of the system ofFIG. 22.

With reference now to FIG. 24 shown is an example of an operational flowrepresenting example operations related to indicating behavior in apopulation cohort, which may serve as a context for introducing one ormore processes and/or devices described herein.

FIG. 25 illustrates an alternative embodiment of the example operationalflow of FIG. 24.

FIG. 26 illustrates an alternative embodiment of the example operationalflow of FIG. 24.

FIG. 27 illustrates an alternative embodiment of the example operationalflow of FIG. 24.

FIG. 28 illustrates an alternative embodiment of the example operationalflow of FIG. 24.

With reference now to FIG. 29, shown is a partial view of an examplecomputer program product that includes a computer program for executinga computer process on a computing device related to indicating behaviorin a population cohort, which may serve as a context for introducing oneor more processes and/or devices described herein.

With reference now to FIG. 30, shown is an example device in whichembodiments may be implemented related to indicating behavior in apopulation cohort, which may serve as a context for introducing one ormore processes and/or devices described herein.

With reference now to FIG. 31, shown is an example device in whichembodiments may be implemented related to indicating behavior in apopulation cohort, which may serve as a context for introducing one ormore processes and/or devices described herein.

The use of the same symbols in different drawings typically indicatessimilar or identical items.

DETAILED DESCRIPTION

FIG. 1 illustrates an example system 100 in which embodiments may beimplemented. The system 100 includes a device 106. The device 106 maycontain, for example, a presentation unit 170, a physiologic activitymeasurement unit 110, an association unit 140, an attributespecification unit 150, and/or a population cohort identification unit166. The device 106 may interact with one or more members of populationcohort 104 and/or population 105. Population cohort 104 may be a part ofa population 105.

FIG. 2 illustrates the example system 100 in which embodiments may beimplemented. The system 100 includes a device 206. Device 206 mayinclude physiologic activity measurement unit 210, which may in turninclude brain activity measurement unit 212, which may in turn includefunctional near-infrared imaging FNIR module 214, functional magneticresonance imaging fMRI module 216, magnetoencephalography MEG module218, electroencephalography EEG module 220, and/or positron emissiontopography PET module 222. Device 206 may also include surrogate markermeasurement unit 230 including iris response module 232, gaze trackingmodule 234, skin response module 236, and/or voice response module 238.Device 206 may also include association unit 240, which in turn mayinclude emotion association module 242, attention association module244, and/or cognition association module 246. Device 206 may alsoinclude population cohort identification unit 266.

Device 206 may also include attribute specification unit 250, which mayin turn include voice specification unit 252, speech specification unit254, non-verbal attribute specification unit 256, facial attributespecification unit 258, clothing specification unit 260, and/or bodyattribute specification unit 262. Device 206 may also includepresentation unit 270, which may in turn include display 272, which mayin turn include desktop display 274 and/or mobile display 276, which mayin turn include pico projector 278 and/or wearable display 280. Memberof population cohort 102, multiple members of population cohort 104,and/or one or more members of population 105 may interact with device206 including presentation unit 270, and/or be monitored by device 206including physiologic activity measurement unit 210.

In FIG. 2, the device 206 is illustrated as possibly being includedwithin a system 100. Of course, virtually any kind of computing devicemay be used to implement the physiologic activity measurement unit 210,surrogate marker measurement unit 230, association unit 340, attributespecification unit 350, and/or presentation unit 264, such as, forexample, a workstation, a desktop computer, a networked computer, aserver, a collection of servers and/or databases, a virtual machinerunning inside a computing device, a mobile computing device, or atablet PC.

Additionally, not all of the physiologic activity measurement unit 210,surrogate marker measurement unit 230, association unit 240, populationcohort identification unit 266, attribute specification unit 250, and/orpresentation unit 264 need be implemented on a single computing device.For example, one or more of the physiologic activity measurement unit210, surrogate marker measurement unit 230, association unit 340,population cohort identification unit 366, attribute specification unit350, and/or presentation unit 264 may be implemented and/or operable ona remote computer, while one or more of these functions are implementedand/or occur on a local computer. Further, aspects of the physiologicactivity measurement unit 210 may be implemented in differentcombinations and implementations than that shown in FIG. 1. For example,functionality of a physiologic activity measurement unit 210 may beincorporated into the surrogate marker measurement unit 230, associationunit 240, population cohort identification unit 266, attributespecification unit 250, and/or presentation unit 264. The associationunit 240, population cohort identification unit 366, and/or attributespecification unit 250 may perform simple data relay functions and/orcomplex data analysis, including, for example, fuzzy logic and/ortraditional logic steps. Further, many methods of searching functionalbrain mapping and/or surrogate marker activity databases known in theart may be used, including, for example, unsupervised pattern discoverymethods, coincidence detection methods, and/or entity relationshipmodeling. In some embodiments, the association unit 240 may processphysiologic activity measurements according to activity profilesavailable as updates through a network. Similarly, in some embodiments,the population cohort identification unit 366 and/or attributespecification unit 250 may process association unit output according topopulation cohort, media content, avatar attribute, and/or avatarprofiles available as updates through a network.

Outputs of physiologic activity measurement unit 210, surrogate markermeasurement unit 230, association unit 240, population cohortidentification unit 266, attribute specification unit 250, and/orpresentation unit 264 may be stored in virtually any type of memory thatis able to store and/or provide access to information in, for example, aone-to-many, many-to-one, and/or many-to-many relationship. Such amemory may include, for example, a relational database and/or anobject-oriented database, examples of which are provided in more detailherein.

FIG. 3 illustrates the example system 100 in which embodiments may beimplemented. The system 100 includes a device 306. The device 306 maycommunicate over a network 374 with a presentation device 364. Device306 may include physiologic activity measurement unit 310, which may inturn include brain activity measurement unit 312, which may in turninclude functional near-infrared imaging fNIR module 314, functionalmagnetic resonance imaging fMRI module 316, magnetoencephalography MEGmodule 318, electroencephalography EEG module 320, and/or positronemission topography PET module 322. Device 306 may also includesurrogate marker measurement unit 330 including iris response module332, gaze tracking module 334, skin response module 336, and/or voiceresponse module 338. Device 306 may also include association unit 340,which in turn may include emotion association module 342, attentionassociation module 344, and/or cognition association module 346. Device306 may also include population cohort identification unit 366.

Device 306 may also include attribute specification unit 350, which mayin turn include voice specification unit 352, which may in turn includespeech specification unit 354. Attribute specification unit 350 mayinclude non-verbal attribute specification unit 356, which may in turninclude facial attribute specification unit 358, clothing specificationunit 360, and/or body attribute specification unit 362.

Presentation device 364 may include presentation unit 370, which may inturn include display 372, which may in turn include desktop display 374and/or mobile display 376, which may in turn include pico projector 378and/or wearable display 380. Member of population cohort 102 and ormultiple members of population cohort 104 may be monitored by device 306including physiologic activity measurement unit 310. Member ofpopulation cohort 102, multiple members of population cohort 104, and/orone or more members of population 105 may interact with presentationdevice 364 including presentation unit 370.

In this way, a member of population cohort 102 or a member of population105, who may be interacting with a presentation device 364 that isconnected through a network 374 with a device 306 (e.g., in a home, anoffice, outdoors and/or in a public environment), may interact with thesystem 100 as if the member of population cohort 102 or member ofpopulation 105 were interacting locally with the device 306 on which theassociation unit 340, population cohort identification unit 366, and/orattribute specification unit 350 is operable. In such an embodiment, thephysiologic activity measurement unit 310 and/or surrogate markermeasurement unit 330 may also be located locally with the member ofpopulation cohort 102 or member of population 105, transmitting outputvia network to a remote association unit 340, population cohortidentification unit 366, and/or attribute specification unit 350.

As referenced herein, the device 306, association unit 340, populationcohort identification unit 366, and/or attribute specification unit 350may be used to perform various data querying and/or recall techniqueswith respect to output of physiologic activity measurement unit 310,and/or output of association unit 340, respectively, in order to forexample, obtain, identify, and/or transmit an avatar attribute and/orpopulation cohort based on a mental state associated with aphysiological activity of member of population cohort 102 or member ofpopulation 105. For example, where the output of physiologic activitymeasurement unit 310 is organized, keyed to, and/or otherwise accessibleusing one or more reference physiologic activity profiles, associationunit 340 may employ various Boolean, statistical, and/or semi-booleansearching techniques to match physiologic activity measurement outputwith one or more appropriate mental states. Similarly, for example,where association unit 340 output is organized, keyed to, and/orotherwise accessible using one or more reference population cohortprofiles, various Boolean, statistical, and/or semi-boolean searchingtechniques may be performed by population cohort identification unit 366to match the mental state of the member of population 105 with one ormore appropriate population cohorts.

Many examples of databases and database structures may be used inconnection with the device 306, association unit 340, and/or attributespecification unit 350. Such examples include hierarchical models (inwhich data is organized in a tree and/or parent-child node structure),network models (based on set theory, and in which multi-parentstructures per child node are supported), or object/relational models(combining the relational model with the object-oriented model).

Still other examples include various types of eXtensible Mark-upLanguage (XML) databases. For example, a database may be included thatholds data in some format other than XML, but that is associated with anXML interface for accessing the database using XML. As another example,a database may store XML data directly. Additionally, or alternatively,virtually any semi-structured database may be used, so that context maybe provided to/associated with stored data elements (either encoded withthe data elements, or encoded externally to the data elements), so thatdata storage and/or access may be facilitated.

Such databases, and/or other memory storage techniques, may be writtenand/or implemented using various programming or coding languages. Forexample, object-oriented database management systems may be written inprogramming languages such as, for example, C++ or Java. Relationaland/or object/relational models may make use of database languages, suchas, for example, the structured query language (SQL), which may be used,for example, for interactive queries for information and/or forgathering and/or compiling data from the relational database(s).

For example, SQL or SQL-like operations over one or more referencephysiologic activity measurement and/or reference mental state may beperformed, or Boolean operations using a reference physiologic activitymeasurement and/or reference mental state may be performed. For example,weighted Boolean operations may be performed in which different weightsor priorities are assigned to one or more of the reference physiologicactivity measurements and/or reference mental states, includingreference physiologic activity measurements and/or reference mentalstates associated with various reference avatar attributes, perhapsrelative to one another. For example, a number-weighted, exclusive-ORoperation may be performed to request specific weightings of desired (orundesired) physiologic activity reference data to be included orexcluded. Reference physiologic activity measurements may include normalphysiological values for an individual in a demographic group respondingto a given stimulus. Such normal physiological activity values may be“normal” relative to the member of population cohort 102, to a group ofpopulation cohort 104, to the entire population cohort 104, to a memberof population 105, and/or the entire population 105. Similarly,reference demographic characteristics may be associated with a generalpopulation or a subpopulation defined by such things as age, gender,ethnicity, or other demographic measure known to those of ordinary skillin the art.

FIG. 4 shows diagrammatic views of the surface of the human brain.Lateral surface of the brain with Brodmann's areas 400 shows variousnumbered areas of a lateral aspect of the human brain. Medial surface ofthe brain with Brodmann's areas 402 shows various numbered areas of themedial aspect of the human brain.

Measuring at least one physiologic activity of a member of populationcohort 102 may include measuring magnetic, electrical, hemodynamic,and/or metabolic activity in the brain.

Magnetoencephalography

One method of measuring at least one physiologic activity may includemeasuring the magnetic fields produced by electrical activity in thebrain via magnetoencephalography (MEG) using magnetometers such assuperconducting quantum interference devices (SQUIDs) or other devices.Such measurements are commonly used in both research and clinicalsettings to, e.g., assist researchers in determining the function ofvarious parts of the brain. Synchronized neuronal currents indicate veryweak magnetic fields that can be measured by magnetoencephalography.However, the magnetic field of the brain is considerably smaller at 10femtotesla (fT) for cortical activity and 103 fT for the human alpharhythm than the ambient magnetic noise in an urban environment, which ison the order of 108 fT. Two essential problems of biomagnetism arise:weakness of the signal and strength of the competing environmentalnoise. The development of extremely sensitive measurement devices suchas SQUIDs facilitates analysis of the brain's magnetic field in spite ofthe relatively low signal versus ambient magnetic signal noise.Magnetoencephalography (and EEG) signals derive from the net effect ofionic currents flowing in the dendrites of neurons during synaptictransmission. In accordance with Maxwell's equations, any electricalcurrent will produce an orthogonally oriented magnetic field. It is thisfield that is measured with MEG. The net currents can be thought of ascurrent dipoles, which are currents having an associated position,orientation, and magnitude, but no spatial extent. According to theright-hand rule, a current dipole gives rise to a magnetic field thatflows around the axis of its vector component.

In order to generate a detectable signal, approximately 50,000 activeneurons are needed. Because current dipoles must have similarorientations to generate magnetic fields that reinforce each other, itis often the layer of pyramidal cells in the cortex, which are generallyperpendicular to its surface, that give rise to measurable magneticfields. Further, it is often bundles of these neurons located in thesulci of the cortex with orientations parallel to the surface of thehead that project measurable portions of their magnetic fields outsideof the head.

Smaller magnetometers are in development, including a mini-magnetometerthat uses a single milliwatt infrared laser to excite rubidium in thecontext of an applied perpendicular magnetic field. The amount of laserlight absorbed by the rubidium atoms varies predictably with themagnetic field, providing a reference scale for measuring the field. Thestronger the magnetic field, the more light is absorbed. Such a systemis currently sensitive to the 70 fT range, and is expected to increasein sensitivity to the 10 fT range. See Physorg.com, “New mini-sensor mayhave biomedical and security applications,” Nov. 1, 2007,http://www.physorg.com/news113151078.html.

Electroencephalography

Another method of measuring at least one physiologic activity mayinclude measuring the electrical activity of the brain by recording fromelectrodes placed on the scalp or, in special cases, subdurally, or inthe cerebral cortex. The resulting traces are known as anelectroencephalogram (EEG) and represent a summation of post-synapticpotentials from a large number of neurons. EEG is most sensitive to aparticular set of post-synaptic potentials: those which are generated insuperficial layers of the cortex, on the crests of gyri directlyabutting the skull and radial to the skull. Dendrites that are deeper inthe cortex, inside sulci, are in midline or deep structures (such as thecingulate gyrus or hippocampus) or that produce currents that aretangential to the skull make a smaller contribution to the EEG signal.

One application of EEG is event-related potential (ERP) analysis. An ERPis any measured brain response that is directly the result of a thoughtor perception. ERPs can be reliably measured usingelectroencephalography (EEG), a procedure that measures electricalactivity of the brain, typically through the skull and scalp. As the EEGreflects thousands of simultaneously ongoing brain processes, the brainresponse to a certain stimulus or event of interest is usually notvisible in the EEG. One of the most robust features of the ERP responseis a response to unpredictable stimuli. This response is known as theP300 (P3) and manifests as a positive deflection in voltageapproximately 300 milliseconds after the stimulus is presented.

The most robust ERPs are seen after many dozens or hundreds ofindividual presentations are averaged together. This technique cancelsout noise in the data allowing only the voltage response to the stimulusto stand out clearly. While evoked potentials reflect the processing ofthe physical stimulus, event-related potentials are caused by higherprocesses, such as memory, expectation, attention, or other changes inmental state.

A two-channel wireless brain wave monitoring system powered by athermo-electric generator has been developed by IMEC (InteruniversityMicroelectronics Centre, Leuven, Belgium). This device uses the bodyheat dissipated naturally from the forehead as a means to generate itselectrical power. The wearable EEG system operates autonomously with noneed to change or recharge batteries. The EEG monitor prototype iswearable and integrated into a headband where it consumes 0.8milliwatts. A digital signal processing block encodes extracted EEGdata, which is sent to a PC via a 2.4-GHz wireless radio link. Thethermoelectric generator is mounted on the forehead and converts theheat flow between the skin and air into electrical power. The generatoris composed of 10 thermoelectric units interconnected in a flexible way.At room temperature, the generated power is about 2 to 2.5-mW or 0.03-mWper square centimeter, which is the theoretical limit of powergeneration from the human skin. Such a device is proposed to associateemotion with EEG signals. See Clarke, “IMEC has a brain wave: feed EEGemotion back into games,” EE Times online,http://www.eetimes.eu/design/202801063 (Nov. 1, 2007).

EEG can be recorded at the same time as MEG so that data from thesecomplimentary high-time-resolution techniques can be combined.

Measuring at least one physiologic activity of a member of populationcohort 102 may also include measuring metabolic or hemodynamic responsesto neural activity. For example, in positron emission tomography (PET),positrons, the antiparticles of electrons, are emitted by certainradionuclides that have the same chemical properties as theirnon-radioactive isotopes and that can replace the latter inbiologically-relevant molecules. After injection or inhalation of tinyamount of these modified molecules, e.g., modified glucose (FDG) orneurotransmitters, their spatial distribution can be detected by aPET-scanner. This device is sensitive to radiation resulting from theannihilation of emitted positrons when they collide withubiquitously-present electrons. Detected distribution informationconcerning metabolism or brain perfusion can be derived and visualizedin tomograms. Spatial resolution is on the order of about 3-6 mm, andtemporal resolution is on the order of several minutes to fractions ofan hour.

Functional Near-infrared Imaging

Another method for measuring physiologic activity is functionalnear-infrared imaging (fNIR). fNIR is a spectroscopic neuro-imagingmethod for measuring the level of neuronal activity in the brain. Themethod is based on neuro-vascular coupling, i.e., the relationshipbetween neuronal metabolic activity and oxygen level (oxygenatedhemoglobin) in blood vessels in proximity to the neurons.

Time-resolved frequency-domain spectroscopy (the frequency-domain signalis the Fourier transform of the original, time-domain signal) may beused in fNIR to provide quantitation of optical characteristics of thetissue and therefore offer robust information about oxygenation. Diffuseoptical tomography (DOT) in fNIR enables researchers to produce imagesof absorption by dividing the region of interest into thousands ofvolume units, called voxels, calculating the amount of absorption ineach (the forward model) and then putting the voxels back together (theinverse problem). fNIR systems commonly have multiple sources anddetectors, signifying broad coverage of areas of interest, and highsensitivity and specificity. fNIR systems today often consist of littlemore than a probe with fiber optic sources and detectors, a piece ofdedicated hardware no larger than a small suitcase and a laptopcomputer. Thus, FNIR systems can be portable; indeed battery operated,wireless continuous wave fNIR devices have been developed at the OpticalBrain Imaging Lab of Drexel University. fNIR employs no ionizingradiation and allows for a wide range of movement; it's possible, forexample, for a subject to walk around a room while wearing a fNIR probe.fNIR studies have examined cerebral responses to visual, auditory andsomatosensory stimuli, as well as the motor system and language, andsubsequently begun to construct maps of functional activation showingthe areas of the brain associated with particular stimuli andactivities.

For example, a fNIR spectroscopy device (fNIRS) has been developed thatlooks like a headband and uses laser diodes to send near-infrared lightthrough the forehead at a relatively shallow depth e.g., (two to threecentimeters) to interact with the brain's frontal lobe. Light ordinarilypasses through the body's tissues, except when it encounters oxygenatedor deoxygenated hemoglobin in the blood. Light waves are absorbed by theactive, blood-filled areas of the brain and any remaining light isdiffusely reflected to fNIRS detectors. See “Technology could enablecomputers to ‘read the minds’ of users,” Physorg.comhttp://www.physorg.com/news110463755.html (1 Oct. 2007).

There are three types of fNIR: (1) CW—continuous wave—In this method,infrared light shines at the same intensity level during the measurementperiod. The detected signal is lower intensity static signal (dcvalued); (2) FD—frequency domain—In this method, input signal is amodulated sinusoid at some frequency and detected output signal haschanges in amplitude and phase; (3) TR—time resolved—In time resolvespectroscopy, a very short pulse is introduced to be measured and thepulse length is usually on the order of picoseconds. The detected signalis usually a longer signal and has a decay time.

In one approach, an infrared imager captures an image of a portion ofthe user. For example, the imager may capture a portion of the user'sforehead. Infrared imaging may provide an indication of blood oxygenlevels which in turn may be indicative of brain activity. With suchimaging, the infrared imager may produce a signal indicative of brainactivity. According to one method, hemoglobin oxygen saturation andrelative hemoglobin concentration in a tissue may be ascertained fromdiffuse reflectance spectra in the visible wavelength range. This methodnotes that while oxygenated and deoxygenated hemoglobin contributions tolight attenuation are strongly variable functions of wavelength, allother contributions to the attenuation including scattering are smoothwavelength functions and can be approximated by Taylor series expansion.Based on this assumption, a simple, robust algorithm suitable for realtime monitoring of the hemoglobin oxygen saturation in the tissue wasderived. This algorithm can be used with different fiber probeconfigurations for delivering and collecting light passed throughtissue. See Stratonnikov et al., “Evaluation of blood oxygen saturationin vivo from diffuse reflectance spectra,” J. Biomed. Optics, vol. 6,pp. 457-467 (2001).

Functional Magnetic Resonance Imaging

Another method of measuring at least one physiologic activity mayinclude measuring blood oxygen level dependent effects by, for example,functional magnetic resonance imaging (fMRI). fMRI involves the use ofmagnetic resonance scanners to produce sets of crosssections—tomograms—of the brain, detecting weak but measurable resonancesignals that are emitted by tissue water subjected to a very strongmagnetic field after excitation with a high frequency electromagneticpulse. Acquired resonance signals can be attributed to their respectivespatial origins, and cross sectional images can be calculated. Thesignal intensity, often coded as a gray value of a picture element,depends on water content and certain magnetic properties of the localtissue. In general, structural MR imaging is used to depict brainmorphology with good contrast and high resolution. Visualizing brainfunction by MRI relies on the relationship between increased neuralactivity of a brain region and increased hemodynamic response or bloodflow to that brain region. The increased perfusion of activated braintissue is the basis of the so-called Blood Oxygenation Level Dependent(BOLD)-effect: hemoglobin, the oxygen carrying molecule in blood, hasdifferent magnetic properties depending on its oxygenation state. Whileoxyhemoglobin is diamagnetic, deoxyhemoglobin is paramagnetic, whichmeans that it locally distorts the magnetic field, leading to a localsignal loss. In activated brain tissue the increased oxygen consumptionis accompanied by a blood flow response. Thus, during activation of abrain region, deoxyhemoglobin is partly replaced by oxyhemoglobin,leading to less distortion of the local magnetic field and increasedsignal intensity. Color-coded statistical parametric activation maps(SPMs) are typically generated from statistical analyses of fMRI timeseries comparing signal intensity during different activation states.

Temporal and spatial resolution of fMRI depends on both scanningtechnology and the underlying physiology of the detected signalintensity changes. Structural images are usually obtained with aresolution of at least 1 mm×1 mm×1 mm voxels (the equivalent of a pixelin a volume), while fMRI voxels typically have edge lengths of about 3-5mm. Temporal resolution of fMRI is on the order of between 1 and 3seconds. The cerebral blood flow (CBF) response to a brain activation isdelayed by about 3-6 seconds. There is a balance between temporal andspatial resolution, allowing whole brain scans in less than 3 seconds,and non-invasiveness, permitting repeated measurements without adverseevents. In addition, the choice of scanning parameters allows increasingone parameter at the expense of the other. Recent fMRI approaches showthat for some neural systems the temporal resolution can be improveddown to milliseconds and spatial resolution can be increased to thelevel of cortical columns as basic functional units of the cortex.

In one embodiment, an fMRI protocol may include fMRI data may beacquired with an MRI scanner such as a 3 T Magnetom Trio Siemensscanner. T2*-weighted functional MR images may be obtained using axiallyoriented echo-planar imaging. For each subject, data may be acquired inthree scanning sessions or functional runs. The first four volumes ofeach session may be discarded to allow for T1 equilibration effects. Foranatomical reference, a high-resolution T1-weighted anatomical image maybe obtained. Foam cushioning may be placed tightly around the side ofthe subject's head to minimize artifacts from head motion. Datapreprocessing and statistical analysis may be carried out using astatistical parametric mapping function, such as SPM99 (StatisticalParametric Mapping, Wellcome Institute of Cognitive Neurology, London,UK). Individual functional images may be realigned, slice-timecorrected, normalized into a standard anatomical space (resulting inisotropic 3 mm voxels) and smoothed with a Gaussian kernel of 6 mm. Inone embodiment, a standard anatomical space may be based on the ICBM 152brain template (MNI, Montreal Neurological Institute). A block-designmodel with a boxcar regressor convoluted with the hemodynamic responsefunction may be used as the predictor to compare activity related to astimulus versus a control object. High frequency noise may be removedusing a low pass filter (e.g., Gaussian kernel with 4.0 s FWHM) and lowfrequency drifts may be removed via a high pass filter. Effects of theconditions for each subject may be compared using linear contrast,resulting in a t-statistic for each voxel. A group analysis may becarried out on a second level using a whole brain random-effect analysis(one-sample t-test). Regions that contain a minimum of five contiguousvoxels thresholded at P<0.001 (uncorrected for multiple comparisons) maybe considered to be active. See Schaefer et al., “Neural correlates ofculturally familiar brands of car manufacturers,” NeuroImage vol. 31,pp. 861-865 (2006).

Mapping Brain Activity

When brain activity data are collected from groups of individuals, dataanalysis across individuals may take into account variation in brainanatomy between and among individuals. To compare brain activationsbetween individuals, the brains are usually spatially normalized to atemplate or control brain. In one approach they are transformed so thatthey are similar in overall size and spatial orientation. Generally, thegoal of this transformation is to bring homologous brain areas into theclosest possible alignment. In this context the Talairach stereotacticcoordinate system is often used. The Talairach system involves acoordinate system to identify a particular brain location relative toanatomical landmarks; a spatial transformation to match one brain toanother; and an atlas describing a standard brain, with anatomical andcytoarchitectonic labels. The coordinate system is based on theidentification of the line connecting the anterior commissure (AC) andposterior commissure (PC)—two relatively invariant fiber bundlesconnecting the two hemispheres of the brain. The AC-PC line defines they-axis of the brain coordinate system. The origin is set at the AC. Thez-axis is orthogonal to the AC-PC-line in the foot-head direction andpasses through the interhemispheric fissure. The x-axis is orthogonal toboth the other axes and points from AC to the right. Any point in thebrain can be identified relative to these axes.

Accordingly, anatomical regions may be identified using the Talairachcoordinate system or the Talairach daemon (TD) and the nomenclature ofBrodmann. The Talairach daemon is a high-speed database server forquerying and retrieving data about human brain structure over theinternet. The core components of this server are a uniquememory-resident application and memory-resident databases. Thememory-resident design of the TD server provides high-speed access toits data. This is supported by using TCP/IP sockets for communicationsand by minimizing the amount of data transferred during transactions. ATD server data may be searched using x-y-z coordinates resolved to 1×1×1mm volume elements within a standardized stereotaxic space. An array,indexed by x-y-z coordinates, that spans 170 mm (x), 210 mm (y) and 200mm (z), provides high-speed access to data. Array dimensions areapproximately 25% larger than those of the Co-planar Stereotaxic Atlasof the Human Brain (Talairach and Tournoux, 1988). Coordinates trackedby a TD server are spatially consistent with the Talairach Atlas. Eacharray location stores a pointer to a relation record that holds datadescribing what is present at the corresponding coordinate. Data inrelation records are either Structure Probability Maps (SP Maps) orTalairach Atlas Labels, though others can be easily added. The relationrecords are implemented as linked lists to names and values for brainstructures. The TD server may be any computing device, such as a SunSparcstation 20 with 200 Mbytes of memory. Such a system provides24-hour access to the data using a variety of client applications.

Some commercially available analysis software such as SPM5 (availablefor download from http://www.fil.ion.ucl.ac.uk/spm/software/spm5/) usesbrain templates created by the Montreal Neurological Institute (MNI),based on the average of many normal MR brain scans. Although similar,the Talairach and the MNI templates are not identical, and care shouldbe given to assigning localizations given in MNI coordinates correctlyto, for example, cytoarchitectonically defined brain areas like theBrodmann areas (BA's), which are regions in the brain cortex defined inmany different species based on its cytoarchitecture. Cytoarchitectureis the organization of the cortex as observed when a tissue is stainedfor nerve cells. Brodmann areas were originally referred to by numbersfrom 1 to 52. Some of the original areas have been subdivided furtherand referred to, e.g., as “23 a” and “23 b.” The Brodmann areas for thehuman brain include the following:

Areas 1, 2 & 3—Primary Somatosensory Cortex (frequently referred to as

Areas 3, 1, 2 by convention)

Area 4—Primary Motor Cortex

Area 5—Somatosensory Association Cortex

Area 6—Pre-Motor and Supplementary Motor Cortex (Secondary Motor Cortex)

Area 7—Somatosensory Association Cortex

Area 8—Includes Frontal eye fields

Area 9—Dorsolateral prefrontal cortex

Area 10—Frontopolar area (most rostral part of superior and middlefrontal gyri)

Area 11—Orbitofrontal area (orbital and rectus gyri, plus part of therostral part of the superior frontal gyrus)

Area 12—Orbitofrontal area (used to be part of BA11, refers to the areabetween the superior frontal gyrus and the inferior rostral sulcus)

Area 13—Insular cortex

Area 17—Primary Visual Cortex (V1)

Area 18—Visual Association Cortex (V2)

Area 19—V3

Area 20—Inferior Temporal gyrus

Area 21—Middle Temporal gyrus

Area 22—Superior Temporal Gyrus, of which the rostral part participatesto Wernicke's area

Area 23—Ventral Posterior cingulate cortex

Area 24—Ventral Anterior cingulate cortex

Area 25—Subgenual cortex

Area 26—Ectosplenial area

Area 28—Posterior Entorhinal Cortex

Area 29—Retrosplenial cingular cortex

Area 30—Part of cingular cortex

Area 31—Dorsal Posterior cingular cortex

Area 32—Dorsal anterior cingulate cortex

Area 34—Anterior Entorhinal Cortex (on the Parahippocampal gyrus)

Area 35—Perirhinal cortex (on the Parahippocampal gyrus)

Area 36—Parahippocampal cortex (on the Parahippocampal gyrus)

Area 37—Fusiform gyrus

Area 38—Temporopolar area (most rostral part of the superior and middletemporal gyri

Area 39—Angular gyrus, part of Wernicke's area

Area 40—Supramarginal gyrus part of Wemicke's area

Areas 41 & 42—Primary and Auditory Association Cortex

Area 43—Subcentral area (between insula and post/precentral gyrus)

Area 44—pars opercularis, part of Broca's area

Area 45—pars triangularis Broca's area

Area 46—Dorsolateral prefrontal cortex

Area 47—Inferior prefrontal gyrus

Area 48—Retrosubicular area (a small part of the medial surface of thetemporal lobe)

Area 52—Parainsular area (at the junction of the temporal lobe and theinsula)

Associating Brain Activity with Brain Function or Mental State

The brain performs a multitude of functions. It is the location ofmemory, including working memory, semantic memory, and episodic memory.Attention is controlled by the brain, as is language, cognitiveabilities, and visual-spatial functions. The brain also receives sensorysignals and generates motor impulses. The frontal lobes of the brain areinvolved in most higher-level cognitive tasks as well as episodic andsemantic memory. There is some degree of lateralization of the frontallobes, e.g., the right frontal lobe is a locus for sustained attentionand episodic memory retrieval, and the left frontal lobe is a locus forlanguage, semantic memory retrieval, and episodic memory encoding.

The cingulated regions of the brain are associated with memory,initiation and inhibition of behavior, and emotion. The parietal regionsof the brain are associated with attention, spatial perception andimagery, thinking involving time and numbers, working memory, skilllearning, and successful episodic memory retrieval. The lateral temporallobe of the brain is associated with language and semantic memoryencoding and retrieval, while the medial temporal lobe is associatedwith episodic memory encoding and retrieval. The occipital temporalregions of the brain are associated with vision and visual-spatialprocessing.

Attention

Attention can be divided into five categories: sustained attention,selective attention, Stimulus-Response compatibility, orientation ofattention, and division of attention. The tasks included in thesustained attention section involved continuous monitoring of differentkinds of stimuli (e.g., somatosensory stimulation). The selectiveattention section includes studies in which subjects selectivelyattended to different attributes of the same set of stimuli (e.g.,attend to color only for stimuli varying with respect to both color andshape). The stimulus-response (SR) compatibility section also includesstudies examining selective attention, with the important differencethat they involve a “conflict component.” In all cases, this isimplemented by employing the Stroop task.

Prefrontal and parietal areas, preferentially in the right hemisphere,are frequently engaged during tasks requiring attention. An fMRI studyinvolving a visual vigilance task was in close agreement with theresults of a PET study showing predominantly right-sided prefrontal andparietal activation. Observed data is consistent with a rightfronto-parietal network for sustained attention. Selective attention toone sensory modality is correlated with suppressed activity in regionsassociated with other modalities. For example, studies have founddeactivations in the auditory cortex during attention area activations.Taken together, the results suggest the existence of a fronto-parietalnetwork underlying sustained attention. Direct support forfronto-parietal interactions during sustained attention has beenprovided by structural equation modeling of fMRI data. Studies on theeffects of attention on thalamic (intralaminar nuclei) and brain stem(midbrain tegmentum) activity have shown that these areas may controlthe transition from relaxed wakefulness to high general attention.

Selective attention is characterized by increased activity in posteriorregions involved in stimulus processing. Different regions seem to beinvolved depending on the specific attribute that is attended to.Studies have shown attentional modulation of auditory regions, andmodulation of activity in the lingual and fusiform gyri during a colorattention task has also been demonstrated. Attending to motion activatesa region in occipito-temporal cortex, and it has also been shown that,in addition to extrastriate regions, attention to motion increasedactivity in several higher-order areas as well. It may be that activityin extrastriate regions may be modulated by prefrontal, parietal andthalamic regions. Similarly, modulation of activity in specificposterior regions is mediated by regions in parietal and anteriorcingulate cortices, as well as the pulvinar. A role of parietal cortex,especially the inferior parietal lobe, in control of selective attentionhas also been suggested. The prefrontal cortex may also play a role inattentional modulation. As long as attentional load is low,task-irrelevant stimuli are perceived and elicit neural activity,however, when the attentional load is increased, irrelevant perceptionand its associated activity is strongly reduced.

The stimulus-response compatibility panel includes selective attentionstudies on the Stroop test. The Stroop test is associated withactivations in the anterior cingulate cortex. SR compatibility studiespoint to a role of both the anterior cingulate and the left prefrontalcortex. See Cabeza et al, “Imaging Cognition II: An Empirical Review of275 PET and fMRI Studies,” J. Cognitive Neurosci., vol. 12, pp. 1-47(2000).

Activation of the thalamic reticular nucleus is also associated withselective attention. See Contreras et al., “Inactivation of theInteroceptive Insula Disrupts Drug Craving and Malaise Indicated byLithium,” Science, vol. 318, pp. 655-658 (26 Oct. 2007).

The category “orientation of attention” includes studies associatingshifts of spatial attention to parietal and prefrontal regions. Anotherstudy found activations in superior parietal regions during a visualsearch for conjunction of features. Based on the similarities inactivation patterns, it appears that serial shifts of attention tookplace during the search task. There is also evidence for a large-scaleneural system for visuospatial attention that includes the rightposterior parietal cortex. PET and fMRI have been employed to studyattentional orienting to spatial locations (left vs. right) and to timeintervals (short vs. long stimulus onset times). Both spatial andtemporal orienting were found to activate a number of brain regions,including prefrontal and parietal brain regions. Other analyses revealedthat activations in the intraparietal sulcus were right-lateralized forspatial attention and left lateralized for temporal attention. Moreover,simultaneous spatial and temporal attention activate mainly parietalregions, suggesting that the parietal cortex, especially in the righthemisphere, is a site for interactions between different attentionalprocesses. Parietal activation has also been demonstrated in an fMRIstudy of nonspatial attention shifting. In addition, the cerebellum hasbeen implicated in attention shifting, and this is consistent with otherfindings of attentional activation of the cerebellum. It has also beenshown that spatial direction of attention can influence the response ofthe extrastriate cortex. Specifically, it was demonstrated that whilemultiple stimuli in the visual field interact with each other in asuppressive way, spatially directed attention partially cancels out thesuppressive effects.

With respect to division of attention, activity in the left prefrontalcortex increases under divided-attention conditions. In this context, itis also relevant to mention that if two tasks activate overlapping brainareas, there may be significant interference effects when the tasks areperformed simultaneously. See Cabeza et al, “Imaging Cognition II: AnEmpirical Review of 275 PET and fMRI Studies,” J. Cognitive Neurosci.,vol. 12, pp. 1-47 (2000).

Perception

Perception processes can be divided into object, face, space/motion,smell and “other” categories. Object perception is associated withactivations in the ventral pathway (ventral brain areas 18, 19, and 37).The ventral occipito-temporal pathway is associated with objectinformation, whereas the dorsal occipito-parietal pathway is associatedwith spatial information. For example, it has been shown that viewingnovel, as well as familiar, line drawings, relative to scrambleddrawings, activated a bilateral extrastriate area near the borderbetween the occipital and temporal lobes. Based on these findings, itappears that this area is concerned with bottom-up construction of shapedescriptions from simple visual features. It has also been shown that aregion termed the “lateral occipital complex” (LO) is selectivelyactivated by different kinds of shapes (e.g., shapes defined by motion,texture, and luminance contours). Greater activity in lingual gyrus(Area 19) and/or inferior fusiform gyrus (Area 37) is seen when subjectsmake judgments about appearance than when they make judgments aboutlocations, providing confirmation that object identity preferentiallyactivates regions in the ventral pathway. Both ventral and dorsalactivations during shape-based object recognition suggests that visualobject processing involves both pathways to some extent (a similarconclusion has been drawn based on network analysis of PET data).

Face perception involves the same ventral pathway as object perception,but there is a tendency for right-lateralization of activations forfaces, but not for objects. For example, bilateral fusiform gyrusactivation is seen for faces, but with more extensive activation in theright hemisphere. Faces are perceived, at least in part, by a separateprocessing stream within the ventral object pathway. In an fMRI study, aregion was identified that is more responsive to faces than to objects,termed the “fusiform face area” or FF area.

Whereas perception of objects and faces tends to preferentially activateregions in the ventral visual pathway, perception of spatial locationtends to selectively activate more dorsal regions located in parietalcortex. Greater activity in the superior parietal lobe (area 7) as wellas in the premotor cortex is seen during location judgments than duringobject judgments. The dorsal pathway is not only associated with spaceperception, but also with action. For example, perception of scripts ofgoal-directed hand action engage parts of the parietal cortex.Comparison have been done of meaningful actions (e.g., pantomime ofopening a bottle) and meaningless actions (e.g., signs from the AmericanSign Language that were unknown to subjects). Whereas meaninglessactions activated the dorsal pathway, meaningful actions activated theventral pathway. Meaningless actions appear to be decoded in terms ofspatiotemporal layout, while meaningful actions are processed by areasthat allow semantic processing and memory storage. Thus, as objectperception, location/action perception may involve both dorsal andventral pathways to some extent.

Activations in the orbitofrontal cortex (where the secondary olfactorycortex is located), particularly in the right hemisphere, and thecerebellum are associated with smelling, as well as increased activityin the primary olfactory cortex (piriform cortex). Odorants (regardlessof sniffing) activate the posterior lateral cerebellum, whereas sniffing(nonodorized air) activate anterior parts of the cerebellum. Thus thecerebellum receives olfactory information for modulating sniffing.Odorants (regardless of sniffing) activate the anterior and lateralorbitofrontal cortex whereas sniffing (even in the absence of odorants)activates the piriform and medial/posterior orbitofrontal cortices. Insum, smell perception involves primarily the orbitofrontal cortex andparts of the cerebellum and its neural correlates can be dissociatedfrom those of sniffing.

With respect to the “other” category, fMRI has been employed to define a“parahippocampal place area” (PPA) that responds selectively topassively viewed scenes. A region probably overlapping with PPA respondsselectively to buildings, and this brain region may respond to stimulithat have orienting value (e.g., isolated landmarks as well as scenes).The neural correlates of music perception have been localized tospecialized neural systems in the right superior temporal cortex, whichparticipate in perceptual analysis of melodies. Attention to changes inrhythm activate Broca's/insular regions in the left hemisphere, pointingto a role of this area in the sequencing of auditory input. Further,studies of “emotional perception” suggest that perception of differentkinds of emotion are based on separate neural systems, with a possibleconvergence in prefrontal regions (area 47). Consistent with the role ofthe amygdala in fear conditioning, the amygdala is more activated forfearful faces relative to happy faces. See Cabeza et al, “ImagingCognition II: An Empirical Review of 275 PET and fMRI Studies,” J.Cognitive Neurosci., vol. 12, pp. 1-47 (2000).

Imagery

Imagery can be defined as manipulating sensory information that comesnot from the senses, but from memory. The memory representationsmanipulated can be in working memory (e.g., holding three spatiallocations for 3 seconds), episodic memory (e.g., retrieving the locationof an object in the study phase), or semantic memory (e.g., retrievingthe shape of a bicycle). Thus, imagery-related contrasts could beclassified within working memory, episodic retrieval, and semanticretrieval sections. Imagery contrasts can be described as visuospatialretrieval contrasts, and vice versa.

A central issue in the field of imagery has been whether those visualareas that are involved when an object is perceived are also involvedwhen an object is imagined. In its strictest form, this idea would implyactivation of the primary visual cortex in the absence of any visualinput. A series of PET experiments provides support for similaritiesbetween visual perception and visual imagery by showing increased bloodflow in Area 17 during imagery. In particular, by comparing tasksinvolving image formation for small and large letters, respectively,these studies provide evidence that imagery activates thetopographically mapped primary visual cortex. A subsequent PET study,involving objects of three different sizes, provides additional supportthat visual imagery activates the primary visual cortex.

Increased activation in extrastriate visual regions is also associatedwith imaging tasks. The left inferior temporal lobe (area 37) is mostreliably activated across subjects (for some subjects the activationextended into area 19 of the occipital lobe). Compared with a restingstate, a left posterior-inferior temporal region was also activated.Moreover, mental imagery of spoken, concrete words has been shown toactivate the inferior-temporal gyrus/fusiform gyrus bilaterally. Thus,right temporal activation may be related to more complex visual imagery.

Color imagery and color perception engage overlapping networks anteriorto region V4 (an area specialized for color perception), whereas areasV1-V4 were selectively activated by color perception. There is anincrease in primary visual-cortex activity during negative imagery, ascompared to neutral imagery. The primary visual cortex therefore appearsto have a role in visual imagery, and emotion appears to affect thequality of the image representations.

Mental rotation of visual stimuli involves lateral parietal areas (BA47and BA40). The bulk of the computation for this kind of mental rotationis performed in the superior parietal lobe. PET has been employed tostudy a mental-rotation task in which subjects were asked to decidewhether letters and digits, tilted in 120°, 180°, or 240°, were innormal or mirror image form. The left parietal cortex is activated inthis task.

Mental “exploration” of maps or routes has been studied using PET,revealing that this task is associated with increased activity in theright superior occipital cortex, the supplementary motor area (SMA) andthe cerebellar vermis. The latter two activations are related to eyemovements, and it appears that the superior occipital cortex has aspecific role in generation and maintenance of visual mental images. Ina subsequent PET study, occipital activation was again observed,although this time the peak was in left middle occipital gyrus. Thisactivation was specific to a task involving mental navigation—staticvisual imagery was not associated with occipital activation. Mentalnavigation tasks appears to tap visual memory to a high extent, andfeedback influences from areas involved in visual memory may activatevisual (occipital) areas during certain imagery tasks.

Thus, visual mental imagery is a function of the visual associationcortex, although different association areas seem to be involveddepending on the task demands. In addition, prefrontal areas have beenactivated in many of the reported comparisons. Partly, these effects maybe driven by eye movements (especially for areas 6 and 8), but otherfactors, such as image generation and combination of parts into a whole,may account for some activations as well.

Neuroanatomical correlates of motor imagery via a mental writing taskimplicate a left parietal region in motor imagery, and, more generally,show similarities between mental writing and actual writing.Similarities between perception and imagery are seen in both musicalimagery and perception. For example, relative to a visual baselinecondition, an imagery task is associated with increased activity in thebilateral secondary auditory cortex. This was so despite the fact thatthe contrast included two entirely silent conditions. Similarly, acomparison of a task involving imaging a sentence being spoken inanother person's voice with a visual control task reveals left temporalactivation. Activation of the supplementary motor area was also seen,suggesting that both input and output speech mechanisms are engaged inauditory mental imagery. See Cabeza et al, “Imaging Cognition II: AnEmpirical Review of 275 PET and fMRI Studies,” J. Cognitive Neurosci.,vol. 12, pp. 1-47 (2000).

Language

Language mapping studies are commonly divided into four categories:spoken and written word recognition crossed with spoken or no-spokenresponse. Word recognition, regardless of input modality and whether ornot a spoken response is required, has consistently been found toactivate areas 21 and 22 in the temporal cortex. In general, thisactivation tends to be bilateral, although in the category of writtenword recognition all activations are left-lateralized. The corticalsurface covered by these areas is most likely made up by severaldistinct regions that can be functionally dissociated. Involvement ofleft superior temporal gyrus/Wernicke's area in word recognition is inagreement with the traditional view implicating this area incomprehension.

Whereas left temporal brain regions have been associated with wordcomprehension, left inferior prefrontal cortex/Broca's area hastraditionally been linked to word production. However, comparingconditions involving spoken response with conditions involving no spokenresponse do not suggest that (left) prefrontal involvement is greaterwhen spoken responses are required. Instead, the major differencebetween these two classes is that conditions involving spoken responsestend to activate the cerebellum to a higher extent. Broca's area isinvolved in word perception, as well as in word production, and inaddition to having an output function, the left prefrontal areas mayparticipate in receptive language processing in the uninjured state. AnfMRI study has shown that cerebellar activation is related to thearticulatory level of speech production.

Visual areas are more frequently involved in the case of written wordrecognition, and regardless of output (spoken/no spoken), written wordrecognition tends to differentially activate left prefrontal andanterior cingulate regions. Moreover, left inferior prefrontalactivation has been associated with semantic processing.

A posterior left temporal region (BA 37) is a multimodal languageregion. Both blind and sighted subjects activate this area duringtactile vs. visual reading (compared to non-word letter strings). Thisarea may not contain linguistic codes per se, but may promote activityin other areas that jointly lead to lexical or conceptual access. Area37 has been activated in several studies of written word recognition butnot in studies of spoken word recognition. Lip-reading activates theauditory cortex in the absence of auditory speech sounds. The activationwas observed for silent speech as well as pseudo-speech, but not fornonlinguistic facial movements, suggesting that lip-reading modulatesthe perception of auditory speech at a prelexical level.

There are few differences between sign language and spoken language, andsign language in bilingual persons activates a similar network as thatunderlying spoken language. The difference in activation in ventraltemporal cortex (area 37) related to sign language appears to relate toan attention mechanism that assigns importance to signing hands andfacial expressions. With respect to the processing of native and foreignlanguages, native-language processing, relative to processing of aforeign language, selectively activates several brain regions leading tothe conclusion that some brain areas are shaped by early exposure to thematernal language, and that these regions may not be activated whenpeople process a language that they have learned later in life. InBroca's area, second languages acquired in adulthood are spatiallyseparated from native languages, whereas second languages acquired at anearly age tend to activate overlapping regions within Broca's area. InWernicke's area, no separation based on age of language acquisition isobserved. Further, fMRI has been used to determine brain activityrelated to aspects of language processing. During phonological tasks,brain activation in males was lateralized to the left inferior frontalgyrus, whereas the pattern was more diffuse for females.

Activation patterns related to the processing of particular aspects ofinformation show that a set of brain regions in the right hemisphere isselectively activated when subjects try to appreciate the moral of astory as opposed to semantic aspects of the story. Brain activationassociated with syntactic complexity of sentences indicates that partsof Broca's area increase their activity when sentences increase insyntactic complexity. See Cabeza et al, “Imaging Cognition II: AnEmpirical Review of 275 PET and fMRI Studies,” J. Cognitive Neurosci.,vol. 12, pp. 1-47 (2000).

Working Memory

Working memory consists of three main components: a phonological loopfor the maintenance of verbal information, a visuospatial sketchpad forthe maintenance of visuospatial information, and a central executive forattentional control. Dozens of functional neuroimaging studies ofworking memory have been carried out. Working memory is associated withactivations in prefrontal, parietal, and cingulate regions. There alsomay be involvement of occipital and cerebellar regions discriminationsbetween different Brodmann's areas.

Working memory is almost always associated with increased activity inthe prefrontal cortex. This activity is typically found in areas 6, 44,9 and 46. Area 44 activations are more prevalent for verbal/numerictasks than for visuospatial tasks, and tend to be lateralized to theleft hemisphere (i.e., Broca's area), suggesting that they reflectphonological processing. Area 6 activations are common for verbal,spatial, and problem-solving tasks, and, hence, they are likely relatedto general working memory operations (i.e., they are not material ortask-specific). In contrast, activations in areas 9 and 46 seem to occurfor certain kinds of working memory tasks but not others. Activations inthese two areas tend to be more prevalent for tasks that requiremanipulation of working memory contents, such as N-back tasks, than fortasks that require only uninterrupted maintenance, such as delayedresponse tasks. Ventrolateral prefrontal regions are involved in simpleshort-term operations, whereas mid-dorsal prefrontal regions performhigher-level executive operations, such as monitoring. Object workingmemory may be left-lateralized while spatial-working memory isright-lateralized.

In addition to prefrontal activations, working memory studies normallyshow activations in parietal regions, particularly areas 7 and 40. Inthe case of verbal/numeric tasks, these activations tend to beleft-lateralized, suggesting that they are related to linguisticoperations. The phonological loop consists of a phonological store,where information is briefly stored, and a rehearsal process, whichrefreshes the contents of this store. Left parietal activations mayreflect the phonological store, whereas left prefrontal activations inarea 44 (Broca's area) may reflect the rehearsal process. When nonverbalmaterials are employed, parietal activations, particularly those in area7, tend to be bilateral, and to occur for spatial but not for objectworking memory. Thus the distinction between a ventral pathway forobject processing and a dorsal pathway for spatial processing may alsoapply to working memory.

Working memory tasks are also associated with anterior cingulate,occipital, and cerebellar activations. Anterior cingulate activationsare often found in Area 32, but they may not reflect working memoryoperations per se. Activity in dorsolateral prefrontal regions (areas 9and 46) varies as a function of delay, but not of readability of a cue,and activity in the anterior cingulate (and in some right ventrolateralprefrontal regions) varies as a function of readability but not of delayof a cue. Thus, the anterior cingulate activation seems to be related totask difficulty, rather than to working memory per se. Occipitalactivations are usually found for visuospatial tasks, and may reflectincreased visual attention under working memory conditions. Cerebellaractivations are common during verbal working memory tasks, particularlyfor tasks involving phonological processing (e.g., holding letters) andtasks that engage Broca's area (left area 44).

Consistent with the idea that mid-dorsal areas 9/46 are involved inhigher-level working memory operations, activations in these areas areprominent in the reasoning and planning tasks. Area 10 activations arealso quite prevalent, and may be related to episodic memory aspects ofproblem-solving tasks (see episodic memory retrieval section above).Tasks involving sequential decisions, such as conceptual reasoning andcard sorting consistently engage the basal ganglia, thalamic, andcerebellar regions. These regions are typical skill learning regions andmay reflect the skill-learning aspects of sequential problem-solvingtasks. Also, the basal ganglia, thalamus, and prefrontal cortex areintimately linked and dysfunction of this circuitry could underlieplanning deficits in Parkinson disease. See Cabeza et al, “ImagingCognition II: An Empirical Review of 275 PET and fMRI Studies,” J.Cognitive Neurosci., vol. 12, pp. 1-47 (2000).

Semantic Memory Retrieval

Semantic memory refers to knowledge we share with other members of ourculture, such as knowledge about the meaning of words (e.g., a banana isa fruit), the properties of objects (e.g., bananas are yellow), andfacts (e.g., bananas grow in tropical climates). Semantic memory may bedivided into two testing categories, categorization tasks and generationtasks. In categorization tasks, subjects classify words into differentcategories (e.g., living vs. nonliving), whereas in generation tasks,they produce one (e.g., word stem completion) or several (for example,fluency tasks) words in response to a cue. Semantic memory retrieval isassociated with activations in prefrontal, temporal, anterior cingulate,and cerebellar regions.

Prefrontal activity during semantic memory tasks frequently found in theleft hemisphere but not in the right. This is so even when the stimuliare nonverbal materials, such as objects and faces. This strikingleft-lateralization is in sharp contrast with the right-lateralizationof prefrontal activity typically observed during episodic memoryretrieval. This asymmetric pattern has been conceptualized in terms of ahemispheric encoding/retrieval asymmetry (HERA) model. This modelconsists of three hypotheses: (1) the left prefrontal cortex isdifferentially more involved in semantic memory retrieval than is theright prefrontal cortex; (2) the left prefrontal cortex isdifferentially more involved in encoding information into episodicmemory than is the right prefrontal cortex; and (3) the right prefrontalcortex is differentially more involved in episodic memory retrieval thanis the left prefrontal cortex. Thus, the left-lateralization ofprefrontal activations supports the first hypothesis of the model. Thesecond and third hypotheses are addressed by episodic memory encodingand episodic memory retrieval testing, respectively, as discussed above.

Within the frontal lobes, activations are found in most prefrontalregions, including ventrolateral (areas 45 and 47), ventromedial (area11), posterior (areas 44 and 6), and mid-dorsal (areas 9 and 46)regions. Activations in ventrolateral regions occur during bothclassification and generation tasks and under a variety of conditions,suggesting that they are related to generic semantic retrievaloperations. In contrast, area 11 activations are more common forclassification than for generation tasks, and could be related to acomponent of classification tasks, such as decision-making. Conversely,activations in posterior and dorsal regions are more typical forgeneration tasks than for classification tasks. Many posterioractivations (areas 44 and 6) occur at or near Broca's area, thus theymay reflect overt or covert articulatory processes during wordgeneration. Activations in dorsal regions (areas 9 and 46) areparticularly frequent for fluency tasks. Because fluency tasks requirethe monitoring of several items in working memory, these activations mayreflect working memory, rather than semantic memory, per se.Accordingly, when subjects complete word stems, areas 9/10 are moreactive for stems with many completions than for stems with fewcompletions. These areas may therefore be involved in selecting amongcompeting candidate responses.

Semantic retrieval tasks are also commonly associated with temporal,anterior cingulate, and cerebellar regions. Temporal activations occurmainly in the left middle temporal gyrus (area 21) and in bilateraloccipito-temporal regions (area 37). Left area 21 is activated not onlyfor words but also pictures and faces, suggesting it is involved inhigher-level semantic processes that are independent of input modality.In contrast, area 37 activations are more common for objects and faces,so they could be related to the retrieval of visual properties of thesestimuli. Anterior cingulate activations are typical for generationtasks. The anterior cingulate—like the dorsal prefrontal cortex—is moreactive for stems with many than with few completions, whereas thecerebellum shows the opposite pattern. The anterior cingulate maytherefore be involved in selecting among candidate responses, while thecerebellum may be involved in memory search processes. Accordinglycerebellar activations are found during single-word generation, but notduring fluency tasks.

The retrieval of animal information is associated with left occipitalregions and the retrieval of tool information with left prefrontalregions. Occipital activations could reflect the processing of thesubtle differences in physical features that distinguish animals,whereas prefrontal activations could be related to linguistic or motoraspects of tool utilization. Animal knowledge activates a more anteriorregion (area 21) of the inferior temporal lobe than the one associatedwith tool knowledge (area 37). Whereas generating color words activatesfusiform areas close to color perception regions, generating actionwords activates a left temporo-occipital area close to motion perceptionregions. Thus knowledge about object attributes is stored close to theregions involved in perceiving these attributes. See Cabeza et al,“Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies,”J. Cognitive Neurosci., vol. 12, pp. 1-47 (2000).

Episodic Memory Encoding

Episodic memory refers to memory for personally experienced past events,and it involves three successive stages: encoding, storage, andretrieval. Encoding refers to processes that lead to the formation ofnew memory traces. Storage designates the maintenance of memory tracesover time, including consolidation operations that make memory tracesmore permanent. Retrieval refers to the process of accessing storedmemory traces. Encoding and retrieval processes are amenable tofunctional neuroimaging research, because they occur at specific pointsin time, whereas storage/consolidation processes are not, because theyare temporally distributed. It is very difficult to differentiate theneural correlates of encoding and retrieval on the basis of the lesiondata, because impaired memory performance after brain damage may reflectencoding deficits, retrieval deficits, or both. In contrast, functionalneuroimaging allows separate measures of brain activity during encodingand retrieval.

Episodic encoding can be intentional, when subjects are informed about asubsequent memory test, or incidental, when they are not. Incidentallearning occurs, for example, when subjects learn information whileperforming a semantic retrieval task, such as making living/nonlivingdecisions. Semantic memory retrieval and incidental episodic memoryencoding are closely associated. Semantic processing of information(semantic retrieval) usually leads to successful storage of newinformation. Further, when subjects are instructed to learn informationfor a subsequent memory test (intentional encoding), they tend toelaborate the meaning of the information and make associations on thebasis of their knowledge (semantic retrieval). Thus, most of the regions(for example, left prefrontal cortex) associated with semantic retrievaltasks are also associated with episodic memory encoding.

Episodic encoding is associated primarily with prefrontal, cerebellar,and medial temporal brain regions. In the case of verbal materials,prefrontal activations are always left lateralized. This patterncontrasts with the right lateralization of prefrontal activity duringepisodic retrieval for the same kind of materials. In contrast, encodingconditions involving nonverbal stimuli sometimes yield bilateral andright-lateralized activations during encoding. Right-lateralizedencoding activations may reflect the use of non-nameable stimuli, suchas unfamiliar faces and textures, but encoding of non-nameable stimulihas been also associated with left-lateralized activations withunfamiliar faces and locations. Contrasting encoding of verbal materialswith encoding of nonverbal materials may speak to the neural correlatesof different materials rather than to the neural correlates of encodingper se.

The prefrontal areas most commonly activated for verbal materials areareas 44, 45, and 9/46. Encoding activations in left area 45 reflectssemantic processing while those in left area 44 reflects rote rehearsal.Areas 9/46 may reflect higher-order working memory processes duringencoding. Activation in left area 9 increases as a function oforganizational processes during encoding, and is attenuated bydistraction during highly organizational tasks. Cerebellar activationsoccur only for verbal materials and show a tendency for rightlateralization. The left-prefrontal/right-cerebellum pattern duringlanguage, verbal-semantic memory, and verbal-episodic encoding tasks isconsistent with the fact that fronto-cerebellar connections are crossed.

Medial-temporal activations are seen with episodic memory encoding andcan predict not only what items will be remembered, but also how wellthey will be remembered. Medial-temporal activations show a clearlateralization pattern: they are left-lateralized for verbal materialsand bilateral for nonverbal materials. Under similar conditions,medial-temporal activity is stronger during the encoding of picturesthan during the encoding of words, perhaps explaining why pictures areoften remembered better than words. In the case of nonverbal materials,medial-temporal activity seems to be more pronounced for spatial thanfor nonspatial information, consistent with the link between thehippocampus and spatial mapping shown by animal research. See Cabeza etal, “Imaging Cognition II: An Empirical Review of 275 PET and fMRIStudies,” J. Cognitive Neurosci., vol. 12, pp. 1-47 (2000).

Episodic Memory Retrieval

Episodic memory retrieval refers to the search, access, and monitoringof stored information about personally experienced past events, as wellas to the sustained mental set underlying these processes. Episodicmemory retrieval is associated with seven main regions: prefrontal,medial temporal, medial parieto-occipital, lateral parietal, anteriorcingulate, occipital, and cerebellar regions.

Prefrontal activations during episodic memory retrieval are sometimesbilateral, but they show a clear tendency for right-lateralization. Theright lateralization of prefrontal activity during episodic memoryretrieval contrasts with the left lateralization of prefrontal activityduring semantic memory retrieval and episodic memory encoding. Leftprefrontal activations during episodic retrieval tend to occur for tasksthat require more reflectively complex processing. These activations maybe related to semantic retrieval processes during episodic retrieval.Semantic retrieval can aid episodic retrieval particularly duringrecall, and bilateral activations tend to be more frequent during recallthan during recognition. Moreover, left prefrontal activity duringepisodic retrieval is associated with retrieval effort, and is morecommon in older adults than in young adults.

Prefrontal activity changes as a function of the amount of informationretrieved during the scan have been measured by varying encodingconditions (e.g., deep vs. shallow), or by altering the proportion ofold items (e.g., targets) during the scan. As more information isretrieved during the scan, prefrontal activity may increase (retrievalsuccess), decrease (retrieval effort), or remain constant (retrievalmode). These three outcomes are not necessarily contradictory; they maycorrespond to three different aspects of retrieval: maintaining anattentional focus on a particular past episode (retrieval mode),performing a demanding memory search (retrieval effort), and monitoringretrieved information (retrieval success).

These different aspects of retrieval may map to distinct prefrontalregions. The region most strongly associated to retrieval mode is theright anterior prefrontal cortex (area 10). A combined PET/ERP studyassociated a right area 10 activation with task-related rather thanitem-related activity during episodic retrieval. Activations associatedwith retrieval effort show a tendency to be left lateralized,specifically in left areas 47 and 10. Bilateral Areas 10, 9, and 46 aresometimes associated with retrieval success. Prefrontal activity is alsoseen to increase with success activations when subjects are warned aboutthe proportion of old and new items during the scan (biasing).

Medial-temporal activations have been seen in the typical pattern ofepisodic retrieval in PET and fMRI studies, for both verbal andnonverbal materials. In contrast with medial-temporal activations duringepisodic encoding, those during episodic retrieval tend to occur in bothhemispheres, regardless of the materials employed. That they aresometimes found in association with retrieval success, but never inassociation with retrieval effort or retrieval mode, suggest that theyare related to the level of retrieval performance. Medial-temporalactivity increases as linear function of correct old word recognition,and this activity may reflect successful access to stored-memoryrepresentations. Further, hippocampal activity has been associated withconscious recollection. Hippocampal activity is also sensitive to thematch between study and test conditions, such as the orientation ofstudy and test objects. However, recollection need not be accurate; forexample in the case of significant hippocampal activations during therecognition of false targets. Accurate recognition yields additionalactivations in a left temporoparietal region, possibly reflecting theretrieval of sensory properties of auditorily studied words. Further,intentional retrieval is not a precondition for hippocampal activity;activations in this area are found for old information encounteredduring a non-episodic task, suggesting that they can also reflectspontaneous reminding of past events.

After the right prefrontal cortex, the most typical region in PET/fMRIstudies of episodic retrieval is the medial parieto-occipital area thatincludes retrosplenial (primarily areas 29 and 30), precuneus (primarilymedial area 7 and area 31), and cuneus (primarily medial areas 19, 18,and 17) regions. The critical role of the retrosplenial cortex in memoryretrieval is supported by evidence that lesions in this region can causesevere memory deficits (e.g., retrosplenial amnesia. The role of theprecuneus has been attributed to imagery and to retrieval success.Retrieval-related activations in the precuneus are more pronounced forimageable than for nonimageable words. However, the precuneus region wasnot more activated for object recall than for word recall.Imagery-related activations are more anterior than activations typicallyassociated with episodic retrieval. The precuneus is activated for bothimageable and abstract words, and for both visual and auditory studypresentations. Thus this region appears to be involved in episodicretrieval irrespective of imagery content. The precuneus cortex is moreactive in a high-target than in low-target recognition condition.

Episodic memory retrieval is also associated with activations in lateralparietal, anterior cingulate, occipital, and cerebellar regions. Lateralparietal regions have been associated with the processing of spatialinformation during episodic memory retrieval and with the perceptualcomponent of recognition. Anterior cingulate activations (areas 32 and24) have been associated with response selection and initiation ofaction. Anterior cingulate activations may be related to languageprocesses because they are more frequent for verbal than for nonverbalmaterials. As expected, occipital activations are more common duringnonverbal retrieval, possibly reflecting not only more extensiveprocessing of test stimuli but also memory-related imagery operations.Cerebellar activations have been associated with self-initiatedretrieval operations. This idea of initiation is consistent with theassociation of cerebellar activations with retrieval mode and effort,rather than with retrieval success.

With respect to context memory, a fusiform region is more active forobject identity than for location retrieval, whereas an inferiorparietal region shows the opposite pattern. Thus the ventral/dorsaldistinction applies also to episodic retrieval. In the time domain,recognition memory (what) has been contrasted with recency memory(when). Medial-temporal regions are more active during item memory thanduring temporal-order memory, whereas dorsal prefrontal and parietalregions are more active during temporal-order memory than during itemmemory. Parietal activations during temporal-order memory suggest thatthe dorsal pathway may be associated not only with “where” but also with“when.”

Prefrontal regions were similarly activated in both recall andrecognition tests. This may signify the use of associative recognition—aform of recognition with a strong recollection component, or to thecareful matching of task difficulty in the two tests. A comparison offree and cued recall found a dissociation in the right prefrontal cortexbetween dorsal cortex (areas 9 and 46), which is more active during freerecall, and the ventrolateral cortex (area 47/frontal insula), which ismore active during cued recall. Thus some of the activations observedduring episodic-memory retrieval tasks may reflect the working-memorycomponents of these tasks. Autobiographic retrieval is associated withactivations along a right fronto-temporal network.

Episodic memory retrieval is associated with activations in prefrontal,medial temporal, posterior midline, parietal, anterior cingulate,occipital, and cerebellar regions. Prefrontal activations tend to beright-lateralized, and have been associated with retrieval mode,retrieval effort, and retrieval success. The engagement of medialtemporal regions has been linked to retrieval success and recollection.Posterior midline activations also seem related to retrieval success.Parietal activations may reflect processing of spatial context, andanterior cingulate activations may reflect selection/initiationprocesses. Cerebellar involvement has been attributed to self-initiatedretrieval. Spatial retrieval engaged parietal regions, and objectretrieval activated temporal regions. Parietal regions are alsoactivated during temporal-order retrieval, suggesting a general role incontext memory. See Cabeza et al, “Imaging Cognition II: An EmpiricalReview of 275 PET and fMRI Studies,” J. Cognitive Neurosci., vol. 12,pp. 1-47 (2000).

Priming

Priming can be divided into perceptual and conceptual priming. Inseveral studies, perceptual priming has been explored by studyingcompletion of word-stems. In the primed condition, it is possible tocomplete the stems with previously presented words, whereas this is notpossible in the unprimed condition. Visual perceptual priming isassociated with decreased activity in the occipital cortex. PET and fMRIstudies on non-verbal visual perceptual priming have revealedpriming-related reduction in activation of regions in the occipital andinferior temporal brain regions. Priming effects can persist over days;repetition priming (item-specific learning) as measured by fMRI showsthat learning-related neural changes that accompany these forms oflearning partly involve the same regions.

Comparisons of blood flow responses associated with novel vs. familiarstimuli (across memory tasks) show that novel stimuli are associatedwith higher activity in several regions, including fusiform gyrus andcuneus. Thus, priming-related reductions in activity in visual areasoccur even after subliminal presentation.

Priming cannot only facilitate perceptual processes, but may alsoinfluence conceptual processes. The primed condition is associated withdecreased activity in several regions, including the left inferiorprefrontal cortex. Similarly, several fMRI studies that have includedrepeated semantic processing of the same items have found reduced leftprefrontal activation associated with the primed condition. Leftprefrontal reduction of activation is not seen when words arenon-semantically reprocessed, suggesting that the effect reflects aprocess-specific change (not a consequence of mere repeated exposure).This process-specific effect can be obtained regardless of theperceptual format of the stimuli (e.g., pictures or words). Many memorytests rely upon a mixture of processes, and even the stem-completiontask, which has been used in several studies of perceptual priming, hasbeen associated with priming-related left prefrontal reductions. Thismay be taken as evidence that this task, too, taps both perceptual andconceptual processes.

With respect to a neural correlate of priming, repeating items duringperformance of the same task, or even during performance of differenttasks, can lead to decreases in the amount of activation present inspecific brain areas. This effect may reflect enhanced processing of theinvolved neurons or/and a specification of the involved neuronalpopulation, resulting in a spatially less diffuse response. See Cabezaet al, “Imaging Cognition II: An Empirical Review of 275 PET and fMRIStudies,” J. Cognitive Neurosci., vol. 12, pp. 1-47 (2000).

Procedural Memory

Procedural memory processes can be divided into three subcategories:conditioning, motor-skill learning, and nonmotor skill learning. Withrespect to conditioning, studies on eye-blink conditioning point to aconsistent role of the cerebellum in this form of learning (e.g.,decreased activity in the cerebellum following conditioning).Conditioning is also associated with increased activity in the auditorycortex.

Motor-skill learning is associated with activation of motor regions.Area 6 is involved, and learning-related changes have also repeatedlybeen demonstrated in the primary motor cortex (area 4). The size of theactivated area in the primary motor cortex increases as a function oftraining. There is also parietal involvement in motor skill learning;fronto-parietal interactions may underlie task performance. With respectto nonmotor skill learning, cerebellar activation is observed acrosstasks, as is consistent involvement of parietal brain regions. This isin line with the pattern observed for motor-skill learning, and theoverlap in activation patterns may reflect common processes underlyingthese two forms of procedural memory. See Cabeza et al, “ImagingCognition II: An Empirical Review of 275 PET and fMRI Studies,” J.Cognitive Neurosci., vol. 12, pp. 1-47 (2000).

Preference

Neural correlates of preference can be detected through neuroimagingstudies. For example, in a simulated buying decision task betweensimilar fast moving consumer goods, only a subject's preferred brandelicited a reduced activation in the dorsolateral prefrontal, posteriorparietal and occipital cortices and the left premotor area (Brodmannareas 9, 46, 7/19 and 6), and only when the target brand was thesubjects' favorite one. Simultaneously, activity was increased in theinferior precuneus and posterior cingulated (BA 7), right superiorfrontal gyrus (BA 10), right supramarginal gyrus (BA 40) and mostpronounced in the ventromedial prefrontal cortex (“VMPFC”, BA 10).

In fMRI analyses, activation of the nucleus accumbens is associated withproduct preference, and the medial prefrontal cortex is associated withevaluation of gains and losses. When these areas of the brain areactivated, subjects bought a product at an accuracy rate of 60%. Inother fMRI analyses, early stage romantic love has been associated withactivation of subcortical reward regions such as the right ventraltegmental area and the dorsal caudate area. Subjects in more extendedromantic love showed more activity in the ventral pallidum. In stillanother fMRI analysis, in subjects experiencing a mistake, activation ofthe rostral anterior cingulated cortex increased in proportion to afinancial penalty linked to the mistake. See Wise, “Thought Police: HowBrain Scans Could Invade Your Private Life,” Popular Mechanics,(November 2007).

With respect to brand discrimination, brain activations in productchoice differ from those for height discrimination, and there is apositive relationship between brand familiarity and choice time. Neuralactivation during choice tasks involves brain areas responsible forsilent vocalization. Decision processes take approximately 1 second asmeasured by magnetoencephalography and can be seen as two halves. Thefirst period involves gender-specific problem recognition processes, andthe second half concerns the choice itself (no gender differences). MEGmeasurements can be categorized in four stages:

Stage 1—V (visual): Activation of the primary visual cortices at around90 ms after stimulus onset.

Stage 2—T (temporal): Neuronal activity predominantly over leftanterior-temporal and middle-temporal cortices at approximately 325 msafter stimulus onset. Some specific activity was also found over theleft frontal and right extra-striate cortical areas.

Stage 3—F (frontal): Activation of the left inferior frontal cortices atabout 510 ms after stimulus onset. These signals are consistent withactivation of Broca's speech area.

Stage 4—P (parietal): Activation of the right posterior parietalcortices (P) at around 885 ms after stimulus onset.

Male brain activity differed from female in the second stage (T) but notin the other three stages (V, F and P). Left anterior temporal activityis present in both groups, but males seem to activate righthemispherical regions much more strongly during memory recall thanfemales do. As noted above, response times also differed for male andfemale subjects. See Amber et al., “Salience and Choice: NeuralCorrelates of Shopping Decisions,” Psychology & Marketing, Vol. 21(4),pp. 247-261 (April 2004).

In an fMRI study, a consistent neural response in the ventromedialprefrontal cortex was associated with subjects' behavioral preferencesfor sampled anonymized beverages. In a brand-cued experiment, brandknowledge of one of the beverages had a dramatic influence on expressedbehavioral preferences and on the measured brain responses. See Kenninget al., “Neuroeconomics: an overview from an economic perspective,”Brain Res. Bull., vol. 67, pp. 343-354 (2005).

In an fMRI study, only the presence of a subject's favorite brandindicating a distinctive mode of decision-making was associated withactivation of regions responsible for integrating emotions. See Kenninget al., “Neuroeconomics: an overview from an economic perspective,”Brain Res. Bull., vol. 67, pp. 343-354 (2005).

Emotion

Various emotions may be identified through detection of brain activity.As discussed below, activation of the anterior insula has beenassociated with pain, distress, and other negative emotional states.Conversely, as discussed below, positive emotional processes arereliably associated with a series of structures representing a rewardcenter, including the striatum and caudate, and areas of the midbrainand cortex to which they project, such as the ventromedial prefrontalcortex, orbitofrontal cortex, and anterior cingulated cortex, as well asother areas such as the amygdala and the insula.

In addition, approval and/or disapproval may be determined based onbrain activity. For example, in an fMRI study,blood-oxygen-level-dependent signal changes were measured in subjectsviewing facial displays of happiness, sadness, anger, fear, and disgust,as well as neutral faces. Subjects were tasked with discriminatingemotional valence (positive versus negative) and age (over 30 versusunder 30) of the faces. During the task, normal subjects showedactivation in the fusiform gyrus, the occipital lobe, and the inferiorfrontal cortex relative to the resting baseline condition. The increasewas greater in the amygdala and hippocampus during the emotional valencediscrimination task than during the age discrimination task. See Gur etal., “An fMRI study of Facial Emotion Processing in Patients withSchizophrenia,” Am. J. Psych., vol. 159, pp. 1992-1999 (2002).

Frustration is associated with decreased activation in the ventralstriatum, and increased activation in the anterior insula and the rightmedial prefrontal cortex by fMRI. See Kenning et al., “Neuroeconomics:an overview from an economic perspective,” Brain Res. Bull., vol. 67,pp. 343-354 (2005).

Fairness, Altruism and Trust

fMRI has been used to show that perceived unfairness correlates withactivations in the anterior insula and the dorsolateral, prefrontalcortex (“DLPFC”). Anterior insula activation is consistently seen inneuroimaging studies focusing on pain and distress, hunger and thirst,and autonomic arousal. Activation of the insula has also been associatedwith negative emotional states, and activation in the anterior insulahas been linked to a negative emotional response to an unfair offer,indicating an important role for emotions in decision-making.

In contrast to the insula region, the DLPFC has been linked to cognitiveprocesses such as goal maintenance and executive control. Thus, DLPFCactivation may indicate objective recognition of benefit despite anemotional perception of unfairness.

Event-related hyperscan-fMRI (“hfMRI” which means that two volunteersare measured parallel in two scanners) has been used to measure theneural correlates of trust. By this method, the caudate nucleus has beenshown to be involved in trust-building and reciprocity in economicexchange. The caudate nucleus is commonly active when learning aboutrelations between stimuli and responses. See Kenning et al.,“Neuroeconomics: an overview from an economic perspective,” Brain Res.Bull., vol. 67, pp. 343-354 (2005).

In a PET study, sanctions against defectors were associated withactivity in reward-processing brain regions. See Kenning et al.,“Neuroeconomics: an overview from an economic perspective,” Brain Res.Bull., vol. 67, pp. 343-354 (2005).

Reward

In an fMRI study, activation changes in the sublenticular extendedamygdala (SLEA) and orbital gyrus were associated with expected valuesof financial gain. Responses to actual experience of rewards increasedmonotonically with monetary value in the nucleus accumbens, SLEA, andthalamus. Responses to prospective rewards and outcomes were generally,but not always, seen in the same regions. Overlaps with activationchanges seen previously in response to tactile stimuli, gustatorystimuli, and euphoria-indicating drugs were found. See Kenning et al.,“Neuroeconomics: an overview from an economic perspective,” Brain Res.Bull., vol. 67, pp. 343-354 (2005).

In another fMRI study, within a group of cooperative subjects theprefrontal cortex showed activation changes when subjects playing ahuman compared to playing a computer. Within a group of non-cooperators,no significant activation changes in the prefrontal cortex were seenbetween computer and human conditions. See Kenning et al.,“Neuroeconomics: an overview from an economic perspective,” Brain Res.Bull., vol. 67, pp. 343-354 (2005).

In an fMRI study, products symbolizing wealth and status were associatedwith increased activity in reward-related brain areas. See Kenning etal., “Neuroeconomics: an overview from an economic perspective,” BrainRes. Bull., vol. 67, pp. 343-354 (2005).

In a PET study, participants were risk averse in gains and risk-seekingin losses; and ambiguity-seeking in neither gains nor losses.Interactions between attitudes and beliefs were associated with neuralactivation changes in dorsomedial and ventromedial brain areas. SeeKenning et al., “Neuroeconomics: an overview from an economicperspective,” Brain Res. Bull., vol. 67, pp. 343-354 (2005).

In an fMRI study, increasing monetary gains were associated withincreased activity in a subcortical region of the ventral striatum in amagnitude-proportional manner. This ventral striatal activation was notevident during anticipation of losses. Actual gain outcomes wereassociated with activation of a region of the medial prefrontal cortex.During anticipation of gain, ventral striatal activation was associatedwith feelings characterized by increasing arousal and positive valence.See Kenning et al., “Neuroeconomics: an overview from an economicperspective,” Brain Res. Bull., vol. 67, pp. 343-354 (2005).

In an fMRI study, activation of parts of the limbic system wereassociated with decisions involving immediate rewards. Activity changesin the lateral prefrontal cortex and posterior parietal cortex wereassociated with inter-temporal choices. Greater relative fronto-parietalactivity was associated with a subject's choice of longer term options.See Kenning et al., “Neuroeconomics: an overview from an economicperspective,” Brain Res. Bull., vol. 67, pp. 343-354 (2005).

Brain Activation By Region

Prefrontal Regions

The prefrontal cortex is involved in almost all high-level cognitivetasks. Prefrontal activations are particularly prominent during workingmemory and memory retrieval (episodic and semantic), and less prevalentduring perception and perceptual priming tasks. This pattern isconsistent with the idea that the prefrontal cortex is involved inworking memory processes, such as monitoring, organization, andplanning. However, some of the same prefrontal regions engaged byworking tasks are also recruited by simple detection tasks that do notinvolve a maintenance component. Thus the prefrontal cortex is notdevoted solely to working memory operations.

Regarding lateralization, prefrontal activations during language,semantic memory retrieval, and episodic memory encoding are usuallyleft-lateralized, those during sustained attention and episodicretrieval are mostly right-lateralized, and those during working memoryare typically bilateral.

With respect to distinctions between different prefrontal areas,ventrolateral regions (areas 45 and 47) are involved in selecting,comparing, or deciding on information held in short-term and long-termmemory, whereas mid-dorsal regions (areas 9 and 46) are involved whenseveral pieces of information in working memory need to be monitored andmanipulated. Area 45/47 activations were found even in simple languagetasks, while activations in areas 9/46 were associated with workingmemory and episodic encoding and retrieval. However, areas 9/46 werealso activated during sustained attention tasks, which do not involvethe simultaneous consideration of several pieces of information. SeeCabeza et al, “Imaging Cognition II: An Empirical Review of 275 PET andfMRI Studies,” J. Cognitive Neurosci., vol. 12, pp. 1-47 (2000).

Humans restrain self-interest with moral and social values. They are theonly species known to exhibit reciprocal fairness, which implies thepunishment of other individuals' unfair behaviors, even if it hurts thepunisher's economic self-interest. Reciprocal fairness has beendemonstrated in the Ultimatum Game, where players often reject theirbargaining partner's unfair offers. It has been shown that disruption ofthe right, but not the left, dorsolateral prefrontal cortex (DLPFC) bylow-frequency repetitive transcranial magnetic stimulation substantiallyreduces subjects' willingness to reject their partners' intentionallyunfair offers, which suggests that subjects are less able to resist theeconomic temptation to accept these offers. Importantly, however,subjects still judge such offers as very unfair, which indicates thatthe right DLPFC plays a key role in the implementation offairness-related behaviors. See Knoch et al., “Diminishing ReciprocalFairness by Disrupting the Right Prefrontal Cortex,” Science, vol. 314,pp. 829-832 (3 Nov. 2006).

Differences across tasks can be found in frontopolar (area 10),opercular (area 44), and dorsal (areas 6 and 8) prefrontal regions.Frontopolar activations were typical for episodic memory retrieval andproblem-solving tasks. In the case of episodic retrieval, they are foundfor both retrieval success and retrieval mode, suggesting they areprobably not related to performance level or task difficulty. Area 10 isinvolved in maintaining the mental set of episodic retrieval, but alsohas an involvement in problem-solving tasks. Activations in left area44, which corresponds to Broca's area, were commonly found for reading,verbal working memory and semantic generation. Right area 44 is engagedby nonverbal episodic retrieval tasks. Area 6 plays a role in spatialprocessing (orientation of attention, space/motion perception andimagery), working memory, and motor-skill learning. Midline area 6activations correspond to SMA and are common for silent reading tasks.Area 8 is involved in problem-solving tasks, possibly reflecting eyemovements. See Cabeza et al, “Imaging Cognition II: An Empirical Reviewof 275 PET and fMRI Studies,” J. Cognitive Neurosci., vol. 12, pp. 1-47(2000).

The frontopolar cortex has been shown to be active during the initialstages of learning, gradually disengaging over the course of learning.Frontopolar cortex activity specifically correlates with the amount ofuncertainty remaining between multiple putative options that subjectsare simultaneously tracking. The frontopolar cortex is also activewhenever subjects depart from an a priori optimal option to checkalternative ones. Thus the frontopolar cortex contribution to learningand exploration appears to be associated with maintaining and switchingback and forth between multiple behavioral alternatives in search ofoptimal behavior. The frontopolar cortex has also been implicated inmemory retrieval, relational reasoning, and multitasking behaviors.These subfunctions are thought to be integrated in the general functionof contingently switching back and forth between independent tasks bymaintaining distractor-resistant representations of postponed tasksduring the performance of another task. For example, the frontopolarcortex is specifically activated when subjects suspend execution of anongoing task set associated with a priori the largest expected futurerewards in order to explore a possibly more-rewarding task set. SeeKeochlin et al., “Anterior Prefrontal Function and the Limits of HumanDecision-Making,” Science, vol. 318, pp. 594-598 (26 Oct. 2007).

Activation of the medial prefrontal cortex and anterior paracingulatecortex indicate that a subject is thinking and acting on the beliefs ofothers, for example, either by guessing partner strategies or whencomparing play with another human to play with a random device, such asa computer partner. Accordingly, these regions may be involved inintention detection, i.e., assessing the meaning of behavior fromanother agent. The tempo-parietal junction is also implicated in thisfunction. Further, publication brand-related bias in the credibility ofambiguous news headlines is associated with activation changes in themedial prefrontal cortex. See Kenning et al., “Neuroeconomics: anoverview from an economic perspective,” Brain Res. Bull., vol. 67, pp.343-354 (2005).

In situations in which people gain some useful good (e.g., money, juice,or other incentive) by using judgment, activation can be observed in theso-called “reward areas” of the brain. Therefore, a “feeling” ofapproval or utility may correlate with the activation in the rewardareas of the brain. Reward areas of the brain include the ventralstriatum and the orbitofrontal prefrontal cortex-amygdala-nucleusaccumbens circuit. Monetary payoffs indicate activation in the nucleusaccumbens. The nucleus accumbens is densely innervated by dopaminergicfibers originating from neurons in the midbrain. Sudden release ofdopamine after an unexpected reward may lead to acceptance of risk.Accordingly, defects in the orbitofrontal cortex-amygdala-nucleusaccumbens reward circuit may accompany extreme risk-seeking behavior.This reward system is also associated with the perception of utility ofobjects.

Cingulate Regions

Cingulate regions can be roughly classified as anterior (for example,areas 32 and 24), central (areas 23 and 31), and posterior (posteriorarea 31, retrosplenial). Posterior cingulate activations areconsistently seen during successful episodic memory retrieval, as areother posterior midline activations (e.g., medial parietal, cuneus,precuneus). Anterior cingulate activations occur primarily in area 32and are consistently found for S-R compatibility (Stroop test), workingmemory, semantic generation, and episodic memory tasks.

There are three main views of the anterior cingulate function:initiation, inhibitory, and motor. According to the initiation view, theanterior cingulate cortex is involved in “attention to action,” that is,in attentional processes required to initiate behavior. This isconsistent with evidence that damage to this region sometimes producesakinetic mutism, that is, an almost complete lack of spontaneous motoror verbal behavior. This is also consistent with the involvement of thisregion in demanding cognitive tasks, such as working memory and episodicretrieval.

The inhibitory view postulates that the anterior cingulate is involvedin suppressing inappropriate responses. This idea accounts very well notonly for its involvement in the Stroop task, in which prepotentresponses must be inhibited, but also in working memory, in whichinterference from previous trials must be controlled. The initiation andinhibition views are not incompatible: the anterior cingulate cortex mayboth initiate appropriate responses and suppress inappropriate ones.Moreover, these views share the idea that the anterior cingulate cortexplays an “active” role in cognition by controlling the operations ofother regions, including the prefrontal cortex.

In contrast, the motor view conceptualizes the anterior cingulate as amore “passive” structure: it receives cognitive/motor “commands” fromvarious regions (for example, prefrontal cortex), and “funnels” them tothe appropriate motor system. This view assumes that different anteriorcingulate regions are engaged, depending on whether responses areocular, manual, or verbal. For example, due to its close connections tothe auditory cortex, area 32 is assumed to play a role in vocalizationand speech. This idea accounts for activations during tasks involvingverbal materials, such as Stroop, semantic generation, and verbalepisodic retrieval tasks. See Cabeza et al, “Imaging Cognition II: AnEmpirical Review of 275 PET and fMRI Studies,” J. Cognitive Neurosci.,vol. 12, pp. 1-47 (2000).

Lying is associated with increased activity in several areas of thecortex, including the anterior cingulate cortex, the parietal cortex,and the superior frontal gyrus. See Henig, “Looking for the Lie,” NewYork Timeshttp://www.nytimes.com/2006/02/05/magazine/05lying.html?pagewanted=print(5 Feb. 2006).

Parietal Regions

Parietal regions are consistently activated during tasks involvingattention, spatial perception and imagery, working memory, spatialepisodic encoding, episodic retrieval, and skill learning. Medialparietal activations are frequently found during episodic memoryretrieval. In general, lateral parietal activations relate either tospatial perception/attention or to verbal working memory storage.Parietal regions may be part of a dorsal occipito-parietal pathwayinvolved in spatial perception, and/or part of a “posterior attentionsystem” involved in disengaging spatial attention. These spatial viewsaccount for parietal activations during spatial tasks of perception,imagery, and episodic encoding, as well as for those duringskill-learning tasks, which, typically, involve an important spatialcomponent.

According to the working memory interpretation, parietal regions areinvolved in the storage of verbal information in working memory. This isconsistent with evidence that left posterior parietal lesions can impairverbal short-term memory. See Cabeza et al, “Imaging Cognition II: AnEmpirical Review of 275 PET and fMRI Studies,” J. Cognitive Neurosci.,vol. 12, pp. 1-47 (2000).

Temporal Regions

The temporal lobes can be subdivided into four broad regions: lateral(insula, 42, 22, 21, and 20), medial (areas 28, 34-36, and hippocampalregions), posterior (area 37), and polar (area 38). Area 38 is likely tohave a very important role in cognition, for example, by linkingfrontal-lobe and temporal-lobe regions.

Lateral temporal activations are consistently found for language andsemantic memory retrieval and are mostly left-lateralized. Spokenword-recognition tasks usually yield bilateral activations, possiblyreflecting the auditory component of these tasks. The involvement of theleft superior and middle temporal gyrus (areas 22 and 21) in languageoperations is consistent with research on aphasic patients. Since area21 is also consistently activated during semantic retrieval tasks—notonly for verbal but also for nonverbal materials—it is possible thatthis area reflects semantic, rather than linguistic, operations. This issupported by the involvement of this region in object perception.

Medial-temporal lobe activations are repeatedly found for episodicmemory encoding and nonverbal episodic memory retrieval. The involvementof medial temporal regions in episodic memory is consistent with lesiondata. Based on PET data, encoding-related activations are more common inanterior hippocampal regions, whereas retrieval-related activations aremore prevalent in posterior hippocampal regions, a pattern described asthe hippocampal encoding/retrieval (HIPER) model. See Cabeza et al,“Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies,”J. Cognitive Neurosci., vol. 12, pp. 1-47 (2000).

Occipito-Temporal Regions

The engagement of temporo-occipital regions (areas 37, 19, 18, and 17)in cognitive tasks seems to be of two kinds: activations associated withperceiving and manipulating visuospatial information, and deactivationsassociated with perceptual priming. Visual processing along the ventralpathway is assumed to be organized hierarchically, with early imageanalyses engaging areas close to the primary visual cortex andhigher-order object recognition processes involving more anterior areas.Consistent with this idea, activations in areas 18 and 19 occur for mostvisuospatial tasks, whereas activations in area 37 are associated withobject processing. For example, area 37 activation is found whensubjects perceive objects and faces, maintain images of objects inworking memory, and intentionally encode objects. Perception-relatedoccipital activations are enhanced by visual attention and theytherefore can be expected during visual-attentional tasks, as well asduring demanding visual-skill learning tasks (e.g., mirror reading).

Most activations in occipito-temporal regions occur during theprocessing of visual information coming from eyes (perception) or frommemory (imagery), and weaken when the same information is repeatedlyprocessed (priming). See Cabeza et al, “imaging Cognition II: AnEmpirical Review of 275 PET and fMRI Studies,” J. Cognitive Neurosci.,vol. 12, pp. 1-47 (2000).

Subcortical Regions

With respect to activations in the basal ganglia, the thalamus, and thecerebellum, basal ganglia activations were common during motor-skilllearning, and the cerebellum was consistently activated in severaldifferent processes. Evolutionary, anatomical, neuropsychological, andfunctional neuroimaging evidence indicates that the cerebellum plays animportant role in cognition. The cognitive role of the cerebellum hasbeen related as motor-preparation, sensory acquisition, timing, andattention/anticipation. Each of these views can account for somecerebellar activations, but not for all of them. For example, the motorpreparation view accounts well for activations during tasks involvingmotor responses, such as word production and conditioning, while thesensory-acquisition view can accommodate activations during perceptualtasks, such as smelling. The timing view accounts for activations duringtasks involving relations between successive events, such asconditioning and skill learning, while the attention/anticipation viewexplains activations during attention and problem solving. See Cabeza etal, “Imaging Cognition II: An Empirical Review of 275 PET and fMRIStudies,” J. Cognitive Neurosci., vol. 12, pp. 1-47 (2000).

Mesolimbic Dopamine System

Activity in the striatum scales directly with the magnitude of monetaryreward or punishment. The striatum is also involved in social decisions,above and beyond a financial component. The striatum also encodesabstract rewards such as positive feeling as a result of mutualcooperation. In addition, the caudate is activated in situations where asubject has an intention to trust another. Emotional processes arereliably associated with a series of structures including the striatumand caudate, and areas of the midbrain and cortex to which they project,such as the ventromedial prefrontal cortex, orbitofrontal cortex, andanterior cingulated cortex, as well as other areas such as the amygdalaand the insula. Indeed, subjects with lesions in the ventromedialprefrontal cortex and having associated emotional deficits are impairedin performing gambling tasks. The anterior insula is associated withincreased activation as unfairness or inequity of an offer is increased.Activation of the anterior insula predicts an Ultimatum Game player'sdecision to either accept or reject an offer, with rejections associatedwith significantly higher activation than acceptances. Activation of theanterior insula is also associated with physically painful, distressful,and/or disgusting stimuli. Thus, the anterior insula and associatedemotion-processing areas may play a role in marking an interaction asaversive and undeserving of trust in the future. See Sanfey, “SocialDecision-Making: Insights from Game Theory and Neuroscience,” Science,vol. 318, pp. 598-601 (26 Oct. 2007).

Activation in the ventral striatum is seen by fMRI when subjects providea correct answer to a question, resulting in a reward. Similarly, awrong answer and no payment results in a reduction in activity (i.e.,oxygenated blood flow) to the ventral striatum. Moreover, activation ofthe reward centers of the brain including the ventral striatum over andabove that seen from a correct response and reward is seen when asubject receives a reward that is known to be greater than that of apeer in the study. Thus, stimulation of the reward center appears to belinked not only to individual success and reward, but also to thesuccess and rewards of others. See BBC news story “Men motivated by‘superior wage,”’ http://news.bbc.co.uk/l/hi/sci/tech/7108347.stm, (23Nov. 2007).

In a multi-round trust game, reciprocity expressed by one playerstrongly predicts future trust expressed by their partner—a behavioralfinding mirrored by neural responses in the dorsal striatum as measuredby fMRI. Analyses within and between brains show two signals—one encodedby response magnitude, and the other by response timing. Responsemagnitude correlates with the “intention to trust” on the next play ofthe game, and the peak of these “intention to trust” responses shiftsits time of occurrence by 14 seconds as player reputations develop. Thistemporal transfer resembles a similar shift of reward prediction errorscommon to reinforcement learning models, but in the context of a socialexchange. See King-Casas et al., “Getting to Know You: Reputation andTrust in a Two-Person Economic Exchange,” Science, vol. 308, pp. 78-83(1 April 2005).

Activity in the head of the caudate nucleus is associated with theprocessing of information about the fairness of a social partner'sdecision and the intention to repay with trust, as measured byhyperscan-fMRI. See Kenning et al., “Neuroeconomics: an overview from aneconomic perspective,” Brain Res. Bull., vol. 67, pp. 343-354 (2005).

Activation of the insular cortex is associated with the perception ofbodily needs, providing direction to motivated behaviors. For example,imaging studies have shown activation of the insula in addicts withcue-indicated drug craving, and activation of the insular cortex hasbeen associated with subjective reports of drug craving. See Contreraset al., “Inactivation of the Interoceptive Insula Disrupts Drug Cravingand Malaise Indicated by Lithium,” Science, vol. 318, pp. 655-658 (26Oct. 2007).

Visual Cortex

The visual cortex is located in and around the calcarine fissure in theoccipital lobe. In one visual cortex study, subjects were shown twopatterns in quick succession. The first appeared for just 15milliseconds, too fast to be consciously perceived by the viewer. Byexamining fMRI images of the brain, a specific image that had beenflashed in front of the subjects could be identified. The informationwas perceived in the brain even if the subjects were not consciouslyaware of it. The study probed the part of the visual cortex that detectsa visual stimulus, but does not perceive it. It encodes visualinformation that the brain does not process as “seen.” See “Mind-readingmachine knows what you see,” NewScientist.comhttp://www.newscientist.com/article.ns?id=dn7304&feedId=online-news_rss20(25 Apr. 2005).

Hippocampus

Activation of the hippocampus can modulate eating behaviors linked toemotional eating and lack of control in eating. Activation of brainareas known to be involved in drug craving in addicted subjects, such asthe orbitofrontal cortex, hippocampus, cerebellum, and striatum,suggests that similar brain circuits underlie the enhanced motivationaldrive for food and drugs seen in obese and drug-addicted subjects. SeeWang et al., “Gastric stimulation in obese subjects activates thehippocampus and other regions involved in brain reward circuitry,” PNAS,vol. 103, pp. 15641-45 (2006).

Surrogate Markers of Mental State

Surrogate markers of mental state may include indicators of attention,approval, disapproval, recognition, cognition, memory, trust, or thelike in response to a stimulus, other than measurement of brain activityassociated with the stimulus.

Examples of surrogate markers may include a skin response to a stimulus;a face pattern indicative of approval, disapproval, or emotional state;eye movements or pupil movements indicating visual attention to anobject; voice stress patterns indicative of a mental state, or the like.Surrogate markers may be used in conjunction with brain activitymeasurements for higher confidence in a predictive or interpretationaloutcome. For example, brain activation of the caudate nucleus incombination with calm voice patterns may increase confidence in apredictor of trust between a subject and a stimulus. Conversely,conflict between brain activity and a surrogate marker may decreaseconfidence in a predictive or interpretational outcome. For example, apattern of activation of the insula diagnostic for fear, together with avisual face image showing a smile may decrease the level of confidencethat the subject is truly frightened by a stimulus. For example, emotionlinks to cognition, motivation, memory, consciousness, and learning anddevelopmental systems. Affective communication depends on complex,rule-based systems with multiple channels and redundancy built into theexchange system, in order to compensate if one channel fails. Channelscan include all five senses: for example, increased heart-rate orsweating may show tension or agitation and can be heard, seen, touched,smelt or tasted. Emotional exchanges may be visible displays of bodytension or movement, gestures, posture, facial expressions or use ofpersonal space; or audible displays such as tone of voice, choice ofpitch contour, choice of words, speech rate, etc. Humans also use touch,smell, adornment, fashion, architecture, mass media, and consumerproducts to communicate our emotional state. Universals of emotion thatcross cultural boundaries have been identified, and cultural differenceshave also been identified. For example ‘love’ is generally categorizedas a positive emotion in Western societies, but in certain Easterncultures there is also a concept for ‘sad love.’ Accordingly, universalemotional triggers may be used to transcend cultural barriers.

When communicating with computers, people often treat new media as ifthey were dealing with real people. They often follow complex socialrules for interaction and modify their communication to suit theirperceived conversation partner. Much research has focused on the use offacial actions and ways of coding them. Speech recognition systems havealso attracted attention as they grow in capability and reliability, andcan recognize both verbal messages conveyed by spoken words, and nonverbal messages, such as those conveyed by pitch contours.

System responses and means of expressing emotions also vary. Innovativeprototypes are emerging designed to respond indirectly, so the user isrelatively unaware of the response: for example by adaptation ofmaterial, such as changing pace or simplifying or expanding content.Other systems use text, voice technology, visual agents, or avatars tocommunicate. See Axelrod et al., “Smoke and Mirrors: Gathering UserRequirements for Emerging Affective Systems,” 26th Int. Conf.Information Technology Interfaces/TI 2004, Jun. 7-10, 2004, Cavtat,Croatia, pp. 323-328.

Skin Response

Mental state may be determined by detection of a skin responseassociated with a stimulus. One skin response that may correlate withmental state and/or brain activity is galvanic skin response (GSR), alsoknown as electrodermal response (EDR), psychogalvanic reflex (PGR), orskin conductance response (SCR). This is a change in the electricalresistance of the skin. There is a relationship between sympatheticnerve activity and emotional arousal, although one may not be able toidentify the specific emotion being elicited. The GSR is highlysensitive to emotions in some people. Fear, anger, startle response,orienting response, and sexual feelings are all among the emotions whichmay produce similar GSR responses. GSR is typically measured usingelectrodes to measure skin electrical signals.

For example, an Ultimate Game study measured skin-conductance responsesas a surrogate marker or autonomic index for affective state, and foundhigher skin conductance activity for unfair offers, and as with insularactivation in the brain, this measure discriminated between acceptancesand rejections of these offers. See Sanfey, “Social Decision-Making:Insights from Game Theory and Neuroscience,” Science, vol. 318, pp.598-601 (26 Oct. 2007). Other skin responses may include flushing,blushing, goose bumps, sweating, or the like.

Face Pattern Recognition

Mental state may also be determined by detection of facial featurechanges associated with a stimulus, via pattern recognition, emotiondetection software, face recognition software, or the like.

For example, an emotional social intelligence prosthetic device has beendeveloped that consists of a camera small enough to be pinned to theside of a pair of glasses, connected to a hand-held computer runningimage recognition software plus association software that can read theemotions these images show. If the wearer seems to be failing to engagehis or her listener, the software makes the hand-held computer vibrate.The association software can detect whether someone is agreeing,disagreeing, concentrating, thinking, unsure, or interested, just from afew seconds of video footage. Previous computer programs have detectedthe six more basic emotional states of happiness, sadness, anger, fear,surprise and disgust. The system can detect a sequence of movementsbeyond just a single facial expression. The association program is basedon a machine-learning algorithm that was trained by showing it more than100 8-second video clips of actors expressing particular emotions. Thesoftware picks out movements of the eyebrows, lips and nose, and trackshead movements such as tilting, nodding, and shaking, which it thenassociates with the emotion the actor was showing. When presented withfresh video clips, the software gets people's emotions right 90 percentof the time when the clips are of actors, and 64 percent of the time onfootage of ordinary people. See “Device warns you if you're boring orirritating,” NewScientisthttp://www.newscientist.com/article/mg19025456.500-device-warns-you-if-youre-boring-or-irritating.html(29 Mar. 2006).

In another approach, an imager, such as a CCD camera, may observeexpressed features of the user. For example, the imager may monitorpupil dilation, eye movement, expression, or a variety of otherexpressive indicators. Such expressive indicators may indicate a varietyof emotional, behavioral, intentional, or other aspects of the user. Forexample, in one approach, systems have been developed for identifying anemotional behavior of a person based upon selected expressiveindicators. Similarly, eye movement and pupil dilation may be correlatedto truthfulness, stress, or other user characteristics.

Eye Movement Analysis

Eye movement or pupil movement can be tested, for example, by measuringuser pupil and/or eye movements, perhaps in relation to items on adisplay. For example, a user's eye movement to a part of the screencontaining an advertisement may be of interest to an advertiser forpurposes of advertisement placement or determining advertisingnoticeability and/or effectiveness within a computerized game world. Forexample, knowing that a user's eyes have been attracted by anadvertisement may be of interest to an advertiser. For example, amerchant may be interested in measuring whether a user notices a virtualworld avatar having particular design characteristics. If the userexhibits eye movements toward the avatar on a display, then the merchantmay derive a mental state from repeated eye movements vis a vis theavatar, or the merchant may correlate eye movements to the avatar withother physiological activity data such as brain activation dataindicating a mental state such as brand preference, approval or reward.

In another embodiment, a smart camera may be used that can captureimages of a user's eyes, process them and issue control commands withina millisecond time frame. Such smart cameras are commercially available(e.g., Hamamatsu's Intelligent Vision System;http://jp.hamamatsu.com/en/product_info/index.html). Such image capturesystems may include dedicated processing elements for each pixel imagesensor. Other camera systems may include, for example, a pair ofinfrared charge coupled device cameras to continuously monitor pupilsize and position as a user watches a visual target moving, e.g.,forward and backward. This can provide real-time data relating to pupilaccommodation relative to objects on a display, which information may beof interest to an entity 170 (e.g.,http://jp.hamamatsu.com/en/rd/publication/scientific_american/common/pdf/scientific_(—)0608.pdf).

Eye movement and/or pupil movement may also be measured by video-basedeye trackers. In these systems, a camera focuses on one or both eyes andrecords eye movement as the viewer looks at a stimulus. Contrast may beused to locate the center of the pupil, and infrared and near-infrarednon-collumnated light may be used to create a corneal reflection. Thevector between these two features can be used to compute gazeintersection with a surface after a calibration for a subject.

Two types of eye tracking techniques include bright pupil eye trackingand dark pupil eye tracking. Their difference is based on the locationof the illumination source with respect to the optics. If theillumination is coaxial with the optical path, then the eye acts as aretroreflector as the light reflects off the retina, creating a brightpupil effect similar to red eye. If the illumination source is offsetfrom the optical path, then the pupil appears dark.

Bright Pupil tracking creates greater iris/pupil contrast allowing formore robust eye tracking with all iris pigmentation and greatly reducesinterference caused by eyelashes and other obscuring features. It alsoallows for tracking in lighting conditions ranging from total darknessto very bright light. However, bright pupil techniques are notrecommended for tracking outdoors as extraneous IR sources may interferewith monitoring.

Eye tracking configurations can vary; in some cases the measurementapparatus may be head-mounted, in some cases the head should be stable(e.g., stabilized with a chin rest), and in some cases the eye trackingmay be done remotely to automatically track the head during motion. Mosteye tracking systems use a sampling rate of at least 30 Hz. Although50/60 Hz is most common, many video-based eye trackers run at 240, 350or even 1000/1250 Hz, which is recommended in order to capture thedetail of the very rapid eye movements during reading, or during studiesof neurology.

Eye movements are typically divided into fixations, when the eye gazepauses in a certain position, and saccades, when the eye gaze moves toanother position. A series of fixations and saccades is called ascanpath. Most information from the eye is made available during afixation, not during a saccade. The central one or two degrees of thevisual angle (the fovea) provide the bulk of visual information; inputfrom larger eccentricities (the periphery) generally is lessinformative. Therefore the locations of fixations along a scanpathindicate what information loci on the stimulus were processed during aneye tracking session. On average, fixations last for around 200milliseconds during the reading of linguistic text, and 350 millisecondsduring the viewing of a scene. Preparing a saccade towards a new goaltakes around 200 milliseconds.

Scanpaths are useful for analyzing cognitive intent, interest, andsalience. Other biological factors (some as simple as gender) may affectthe scanpath as well. Eye tracking in human-computer interactiontypically investigates the scanpath for usability purposes, or as amethod of input in gaze-contingent displays, also known as gaze-basedinterfaces.

There are two primary components to most eye tracking studies:statistical analysis and graphic rendering. These are both based mainlyon eye fixations on specific elements. Statistical analyses generallysum the number of eye data observations that fall in a particularregion. Commercial software packages may analyze eye tracking and showthe relative probability of eye fixation on each feature on an avatar.This allows for a broad analysis of which avatar elements receivedattention and which ones were ignored. Other behaviors such as blinks,saccades, and cognitive engagement can be reported by commercialsoftware packages. Statistical comparisons can be made to test, forexample, competitors, prototypes or subtle changes to an avatar. Theycan also be used to compare participants in different demographicgroups. Statistical analyses may quantify where users look, sometimesdirectly, and sometimes based on models of higher-order phenomena (e.g.,cognitive engagement).

In addition to statistical analysis, it is often useful to providevisual depictions of eye tracking results. One method is to create avideo of an eye tracking testing session with the gaze of a participantsuperimposed upon it. This allows one to effectively see through theeyes of the consumer during interaction with a target medium. Anothermethod graphically depicts the scanpath of a single participant during agiven time interval. Analysis may show each fixation and eye movement ofa participant during a search on a virtual shelf display of breakfastcereals, analyzed and rendered with a commercial software package. Forexample, a different color may represent one second of viewing time,allowing for a determination of the order in which products are seen.Analyses such as these may be used as evidence of specific trends invisual behavior.

A similar method sums the eye data of multiple participants during agiven time interval as a heat map. A heat map may be produced by acommercial software package, and shows the density of eye fixations forseveral participants superimposed on the original stimulus, for example,an avatar on a magazine cover. Red and orange spots represent areas withhigh densities of eye fixations. This allows one to examine whichregions attract the focus of the viewer.

Commercial eye tracking applications include web usability, advertising,sponsorship, package design and automotive engineering. Eye trackingstudies may presenting a target stimulus to a sample of consumers whilean eye tracker is used to record the activity of the eye. Examples oftarget stimuli may include avatars in the context of websites,television programs, sporting events, films, commercials, magazines,newspapers, packages, shelf displays, consumer systems (ATMs, checkoutsystems, kiosks), and software. The resulting data can be statisticallyanalyzed and graphically rendered to provide evidence of specific visualpatterns. By examining fixations, saccades, pupil dilation, blinks, anda variety of other behaviors, researchers can determine a great dealabout the effectiveness of a given avatar in a given medium orassociated with a given product.

A prominent field of eye tracking research is web usability. Whiletraditional usability techniques are often quite powerful in providinginformation on clicking and scrolling patterns, eye tracking offers theability to analyze user interaction between the clicks. This providesinsight into which features are the most eye-catching, which featurescause confusion, and which ones are ignored altogether. Specifically,eye tracking can be used to assess impressions of an avatar in thecontext of search efficiency, branding, online advertisement, navigationusability, overall design, and/or many other site components. Analysesmay target an avatar on a prototype or competitor site in addition tothe main client site.

Eye tracking is commonly used in a variety of different advertisingmedia. Commercials, print ads, online ads, and sponsored programs areall conducive to analysis with eye tracking technology. Analyses mayfocus on visibility of a target avatar, product, or logo in the contextof a magazine, newspaper, website, virtual world, or televised event.This allows researchers to assess in great detail how often a sample ofconsumers fixates on the target avatar, logo, product, or advertisement.In this way, an advertiser can quantify the success of a given campaignin terms of actual visual attention.

Eye tracking also provides avatar designers with the opportunity toexamine the visual behavior of a consumer while interacting with atarget avatar. This may be used to analyze distinctiveness,attractiveness and the tendency of the avatar to be chosen forrecognition and/or purchase. Eye tracking can be used while the targetavatar is in the prototype stage. Prototype avatars can be are testedagainst each other and against competitors to examine which specificelements are associated with high visibility and/or appeal.

Another application of eye tracking research is in the field ofautomotive design. Eye tracking cameras may be integrated intoautomobiles to provide the vehicle with the capacity to assess inreal-time the visual behavior of the driver. The National HighwayTraffic Safety Administration (NHTSA) estimates that drowsiness is theprimary causal factor in 100,000 police-reported accidents per year.Another NHTSA study suggests that 80% of collisions occur within threeseconds of a distraction. By equipping automobiles with the ability tomonitor drowsiness, inattention, and cognitive engagement driving safetycould be dramatically enhanced. Lexus® claims to have equipped its LS460 automobile with the first driver monitor system in 2006, providing awarning if the driver takes his or her eye off the road.

Eye tracking is also used in communication systems for disabled persons,allowing the user to speak, mail, surf the web and so on with only theeyes as tool. Eye control works even when the user has involuntary bodymovement as a result of cerebral palsy or other disability, and/or whenthe user wears glasses.

Eye movement or pupil movement may be gauged from a user's interactionwith an application.

An example of a measure of pupil movement may be an assessment of thesize and symmetry of a user's pupils before and after a stimulus, suchas light or focal point. In one embodiment, where the user interactswith a head mounted display, the display may include image capturingfeatures that may provide information regarding expressive indicators.Such approaches have been described in scanned-beam display systems suchas those found in U.S. Pat. No. 6,560,028.

Voice Stress Analysis

Voice stress analysis (VSA) technology records psycho-physiologicalstress responses that are present in the human voice when a personexperiences a psychological stress in response to a stimulus.Psychological stress may be detected as acoustic modifications in thefundamental frequency of a speaker's voice relative to normal frequencymodulation of the vocal signal between 8-14 Hz during speech in anemotionally neutral situation. In situations involving a stressresponse, the 8-14 Hz modulation may decrease as the muscles surroundingthe vocal cords contract in response to the reaction.

VSA typically records an inaudible component of human voice, commonlyreferred to as the Lippold Tremor. Under normal circumstances, thelaryngeal muscles are relaxed, producing recorded voice at approximately12 Hz. Under stress however, the tensed laryngeal muscles produce voicesignificantly lower than normal. The higher the stress, the lower downthe Hertz scale voice waves are produced. One application for VSA is inthe detection of deception.

Dektor Counterintelligence manufactured the PSE 1000, an analog machinethat was later replaced by the PSE 2000. The National Institute Of TruthVerification (NITV) then produced and marketed a digital applicationbased on the McQuiston-Ford algorithm. The primary commercial suppliersare Dektor (PSE5128-software); Diogenes (Lantern-software); NITV (CVSASoftware); and Baker (Baker-software).

VSA is distinctly different from LVA (Layered Voice Analysis). LVA isused to measure different components of voice, such as pitch and tone.LVA is available in the form of hand-held devices and software. LVAproduces readings such as ‘love,’ excitement, and fear.

One example of a commercially available layered voice analysis system isthe SENSE system, sold by Nemesysco Ltd (Natania, Israel). SENSE cananalyze different layers within the voice, using multiple parameters toanalyze each speech segment. SENSE can detect various cognitive states,such as whether a subject is excited, confused, stressed, concentrating,anticipating a response, or unwillingly sharing information. Thetechnology also can provide an in-depth view of the subject's range ofemotions, including those relating to love. SENSE technology can befurther utilized to identify psychological issues, mental illness, andother behavioral patterns. The LVA technology is the security version ofthe SENSE technology, adapted to identify the emotional situations asubject is expected to have during formal/security investigations.

The SENSE technology is made up of 4 sub-processes:

1. The vocal waveform is analyzed to measure the presence of localmicro-high frequencies, low frequencies, and changes in their presencewithin a single voice sample.

2. A precise frequency spectrum of the vocal input is sampled andanalyzed.

3. The parameters gathered by the previous steps are used to create abaseline profile for the subject.

4. The new voice segments to be tested are compared with the subject'sbaseline profile, and the analysis is generated.

This input can be further processed by statistical learning algorithmsto predict the probability of a deceptive or fraudulent sentence in asubject's speech. Another layer that is used in certain applicationsevaluates the conversation as a whole, and produces a final risk or QAvalue.

The SENSE technology can detect the following emotional and cognitivestates:

Excitement Level: Each of us becomes excited (or depressed) from time totime. SENSE compares the presence of the Micro-High-frequencies of eachsample to the basic profile to measure the excitement level in eachvocal segment.

Confusion Level: Is your subject sure about what he or she is saying?SENSE technology measures and compares the tiny delays in a subject'svoice to assess how certain he or she is.

Stress Level: Stress may include the body's reaction to a threat, eitherby fighting the threat, or by fleeing. However, during a spokenconversation neither option may be available. The conflict caused bythis dissonance affects the micro-low-frequencies in the voice duringspeech.

Thinking Level: How much is your subject trying to find answers? Mighthe or she be “inventing” stories?

S.O.S.: (Say Or Stop)—Is your subject hesitating to tell you something?

Concentration Level: Extreme concentration might indicate deception.

Anticipation Level: Is your subject anticipating your responsesaccording to what he or she is telling you?

Embarrassment Level: Is your subject feeling comfortable, or does hefeel some level of embarrassment regarding what he or she is saying?

Arousal Level: What triggers arousal in the subject? Is he or sheinterested in an object? Aroused by certain visual stimuli?

Deep Emotions: What long-standing emotions does a subject experience?

Is he or she “excited” or “uncertain” in general?

SENSE's “Deep” Technology: Is a subject thinking about a single topicwhen speaking, or are there several layers to a response (e.g.,background issues, something that may be bothering him or her, planning,or the like). SENSE technology can detect brain activity operating at apre-conscious level.

The speaking mechanism is one of the most complicated procedures thehuman body is capable of. First, the brain has to decide what should besaid, then air is pushed from the lungs upward to the vocal cords, thatmust vibrate to produce the main frequency. Now, the vibrated airarrives to the mouth.

The tongue, the lips, the teeth, and the nose space turns the vibratedair into the sounds that we recognize as phrases. The brain is closelymonitoring all these events, and listens to what comes out; if we speaktoo softly, too loudly, and if it is understandable to a listener. SENSETechnology ignores what your subject is saying, and focuses only on whatthe brain is broadcasting.

Humans, unlike other mammals, are capable of predicting or imagining thefuture. Most people can tell whether or not a certain response willcause them pleasure or pain. Lying is not a feeling, it is a tool. Thefeeling structure around it will be the one causing us to lie, andunderstanding the differences is crucial for making an analysis.

The SENSE technology differentiates among 5 types of lies:

1. Jokes—Jokes are not so much lies as they are untruths, used toentertain. No long gain profit or loss will be earned from it, andusually, little or no extra feelings will be involved.

2. White Lies—You know you don't want to say the truth, as it may hurtsomeone else. White lies are lies, but the teller usually experienceslittle stress or guilt.

3. Embarrassment Lies—Same as for white lies, but this time directedinternally. Nothing will be lost except the respect of the listener,most likely for the short term.

4. Offensive Lies—This is a unique lie, for it's intention is to gainsomething extra that could not be gained otherwise.

5. Defensive Lie—The common lie to protect one's self.

The SENSE technology IS the old “Truster” technology, with severaladditions and improvements. The old Truster was all about emotions inthe context of Truth/Lie; SENSE looks at emotions in general.

When people get sexually aroused or feel “in love,” the pupils getwider, the lips get reddish, the skin of the face gets red. The voicechanges too. Increased excitement makes the whole voice higher and moreconcentrated. The SENSE technology can detect the increased excitementand the associated heightened concentration and anticipation.

While each of the above described approaches to providing expressiveindicators has been described independently, in some approaches, acombination of two or more of the above described approaches may beimplemented to provide additional information that may be useful inevaluating user behavior and/or mental state.

Specifying a Cohort-Linked Avatar Attribute and/or a Cohort-LinkedAvatar

FIG. 5 illustrates an operational flow 500 representing exampleoperations related to specifying an avatar attribute. In FIG. 5 and infollowing figures that include various examples of operational flows,discussion and explanation may be provided with respect to theabove-described system environments of FIGS. 1-4, and/or with respect toother examples and contexts. However, it should be understood that theoperational flows may be executed in a number of other environment andcontexts and/or in modified versions of FIGS. 1-3. Also, although thevarious operational flows are presented in the sequences illustrated, itshould be understood that the various operations may be performed inother orders than those which are illustrated, or may be performedconcurrently.

After a start operation, operation 510 depicts presenting at least onecharacteristic to at least one member of a population cohort. Forexample, an attribute specification unit 250 can transmit acharacteristic for display to a member of population cohort 102 on apresentation unit and/or display 272. Optionally, a presented avatarcharacteristic may be encountered during an interaction of the member ofpopulation cohort 102 with a virtual world; alternatively, thecharacteristic may be one presented in a real world context, forsubsequent incorporation into an avatar. In one embodiment, a clothingspecification unit 260 can present a photograph of a particular item ofclothing independent of an avatar via mobile display 276. In anotherembodiment, a facial attribute specification unit 258 can present an eyeshape and color to a member of population cohort 102 in the context ofan avatar in an online game via a desktop display 274.

For example, a device 106, attribute specification unit 250, and/orpresentation unit 270 may present at least one characteristic to atleast one member of a population cohort 102. In one embodiment, a bodyattribute specification unit 362 may communicate via a network 374, forexample, with a presentation device 364 to present a characteristic tomember of population cohort 102. A population cohort may include an adhoc population cohort, or an established population cohort such as anage-defined demographic group.

In another embodiment, a speech specification unit of the device 306 maypresent a speech characteristic such as accent, dialect, tone, or pitchto a member of population cohort 102 via, for example, a personalcommunication device such as a video-capable cellular phone. Forexample, the attribute specification unit 350 may initiate thepresentation of an avatar with a particular characteristic such as ahair color or hair style to a member of population cohort 102 inresponse to the member of population cohort 102 signing on to a socialnetworking website. The presentation may be through, for example, awidget on the social networking website.

It should be understood that characteristics may be profitably combinedto provide feedback about composite features of presented attributesand/or avatars. For example, an attribute specification unit 350 maypresent a composite voice, facial attribute, clothing attribute, andbody attribute characteristic to a member of population cohort 102 viaan online shopping experience, such as a virtual personal shopper.

Verbal attributes or characteristics may be presented, such as foreignlanguage or accented speech, such as a southern accent, a Boston accent,a Spanish accent, a British accent, or the like. Such voice variationsmay be computer-detectable and/or computer-implemented. See, forexample, U.S. Pat. No. 7,263,489 “Detection of characteristics ofhuman-machine interactions for dialog customization and analysis.”Prosodic features of speech such as intonation, stress, and otherparalinguistic features of speech such as voice quality, emotion, andspeaking style may also be presented as characteristic.

A characteristic may also include paralanguage, a.k.a., vocalics, whichinvolves nonverbal cues of the voice. Various acoustic properties ofspeech such as tone, pitch, accent, or the like, collectively known asprosody, can be presented as nonverbal characteristics. Paralanguage maybe used to present a characteristic, as well as voice qualitiesincluding volume, pitch, tempo, rhythm, articulation, resonance,nasality, and/or accent. Vocalization cues also may be presented as thecharacteristic, including emotions expressed during or associated withspeech, such as laughing, crying, and/or yawning. Vocalization cues alsomay include delivery nuances such as volume and/or pitch modulation suchas whispering and shouting. Vocalization cues also may include vocalsegregates such as “um” in between spoken expressions, or “uh-huh,”“like,” “no way,” or other phrase in response to another's speech toindicate comprehension, to punctuate speech, and/or to manage contactduring dialogue. See, for example, U.S. Pat. No. 6,356,868, “Voiceprintidentification system.”

Alternatively, a non-verbal attribute or characteristic includingphysical appearance and/or non-verbal communication may be presented.For example, body language such as use of American Sign Language may bepresented as the characteristic.

Non-verbal communication characteristics may also include, for example,a facial expression, a gesture, a gaze, and/or a posture. Acharacteristic may also include clothing, hairstyle, adornment, shoes,and/or other communicative props; or even architecture, a symbol, and/oran infographic.

Kinesic behaviors such as body movements, facial expressions, andgestures may also be presented characteristics. Kinesic behaviors mayinclude, mutual gaze, smiling, facial warmth or pleasantness, childlikebehaviors, direct body orientation, and the like. An attributespecification unit 250 and/or presentation unit 270 can present amovement characteristic such as a kineme, which is a unit of visualexpression analogous to a phoneme, a unit of speech. Presentablegestures may include emblems, illustrators, affect displays, regulators,and/or adaptors. An emblem is a gesture with a direct verbal translationsuch as a wave of the hand; an illustrator is a gesture that depicts aconcept that is substantially simultaneously spoken, such as turning animaginary steering wheel while speaking about driving; an affect displayis a gesture that conveys emotions, such as a smile or a frown; aregulator is a gesture that controls interaction such as a “shhh” signplacing an index finger vertically at the center of the lips; andfinally, an adaptor is a gesture that facilitates release of bodytension, such as quick, repetitive leg movements or stretching.

A presented characteristic may also include an advertising symbol and/ora brand name, design, symbol, or logo (e.g., trademarks of corporationssuch as a Bic® pen, a Rolex® watch, a McDonald's® restaurant, a Hermes®scarf, a Louis Vuitton® bag, or a Les Paul® guitar). Service marks mayalso be presented as a characteristic.

A presented characteristic may also include, for example, acharacteristic that is not typically presented in the context of anavatar, and/or a characteristic that an avatar cannot have.

Operation 520 depicts measuring at least one physiologic activity of theat least one member of the population cohort, the at least onephysiologic activity proximate to the at least one characteristic. Forexample, a physiologic activity measurement unit 210, brain activitymeasurement unit 212, and/or surrogate marker measurement unit 230 maymeasure at least one physiologic activity of the at least one member ofthe population cohort, the at least one physiologic activity proximateto the at least one characteristic. For example, during presentation ofthe characteristic to the member of the population cohort 102, a fNIRmodule may detect brain activity in the member of the population cohort102 indicative of approval of the characteristic. Of course othermeasurement methods may be used including fMRI, MEG, EEG, singly or incombination. Optionally, surrogate markers of brain activity may beemployed to detect physiologic activity, such as a voice response to acharacteristic, an eye movement response to an object, a skin responseto an object, or the like. Detected brain activity may be proximate intime to presentation and/or viewing of the characteristic by the memberof the population cohort 102, as determined by one or more of brainactivity indicative of visual activity (e.g., activity in the visualcortex), selective attention (e.g., activity in fronto-parietal areas),and/or perception (e.g., activity in the ventral pathway). Detectedphysiologic activity may be determined to be proximate in time using aneye movement or gaze tracking measurement to identify times when amember of the population cohort 102 looks at a characteristic. In oneembodiment, eye movement or gaze tracking data may be matched with atime course of brain activity to associate a particular brain areaactivation with visual contact of the member of the population cohort102 with a presented characteristic.

As used herein, the term “proximate” may refer to “proximate in time,”for example, a physiologic measurement that is proximate in time to apresentation of a characteristic to a member of population cohort 102and/or member of population 105.

In another embodiment, eye movement or gaze tracking data may be matchedwith a time course of voice response, skin response, or other surrogatemarker of brain activity to associate a particular physiologic activitywith visual contact of the member of the population cohort 102 with apresented characteristic.

In the context of storing physiologic activity measurement data, itshould be understood that a data signal may first be encoded and/orrepresented in digital form (i.e., as digital data), prior to anassignment to at least one memory. For example, a digitally-encodedrepresentation of user eye movement data may be stored in a localmemory, or may be transmitted for storage in a remote memory.

Thus, an operation may be performed relating either to a local or remotestorage of the digital data, or to another type of transmission of thedigital data. Of course, as discussed herein, operations also may beperformed relating to accessing, querying, processing, recalling, orotherwise obtaining the digital data from a memory, including, forexample, transmission of the digital data from a remote memory.Accordingly, such operation(s) may involve elements including at leastan operator (e.g., either human or computer) directing the operation, atransmitting computer, and/or a receiving computer, and should beunderstood to occur within the United States as long as at least one ofthese elements resides in the United States.

Operation 530 depicts associating the at least one physiologicalactivity with at least one mental state. For example, a device 206and/or association unit 240 may associate the at least one physiologicactivity with at least one mental state. For example, association unit240 may associate approval with a pattern of activation of the rewardcenter, including for example, the striatum and caudate, and areas ofthe midbrain and cortex to which they project, such as the ventromedialprefrontal cortex, orbitofrontal cortex, and anterior cingulated cortex.In one embodiment, an association unit 240 may search a database offunctional brain mapping that contains information about which brainregions are associated with particular mental states and/or functions.Such a database search may be keyed to brain activation pattern, and/orto mental state and/or function. For example, an association unit 240may search measured brain activation information and/or surrogate markerinformation for data consistent with trust. For example, a measuredactivation of a region of the brain associated with trust, such as thecaudate nucleus, may be associated with trust as the at least one mentalstate. Alternatively or in addition, measurement of a surrogate markersuch as an approving voice response proximate to the at least onecharacteristic may be associated with approval as the at least onemental state by an association unit 140.

Operation 540 depicts specifying at least one avatar attribute based onthe at least one mental state. For example, a device 206 and/orattribute specification unit 250 may specify at least one avatarattribute based on the at least one mental state. For example, a device206 and/or attribute specification unit 250 may specify at least onefacial feature or facial dimension as an avatar attribute for use in anavatar based on an approving mental state identified in association witha measured physiologic activity proximate to a presented characteristic.In one embodiment, an association of a characteristic with a high degreeof attention on the part of a member of population cohort 102 mayprovide the basis for an attribute specification unit 250 to specify thecharacteristic as an attribute for incorporation into a cohort-linkedavatar.

In another embodiment, a voice specification unit 252 may specify aparticular tonal quality of voice as the at least one avatar attributebased on an association between the tonal voice quality and a positiveemotional response as measured by a brain activity measurement unit 212and/or a surrogate marker measurement unit 230 and as associated byassociation unit 240. In another embodiment, a non-verbal specificationunit 356 may specify an object as an avatar attribute based on apositive mental state associated with the object by emotion associationmodule 342, for example.

In another embodiment, an attribute specification unit 250 may specifyan attribute that is a variant of the initially-presented characteristicthat provided the basis for the identification of mental state. In thiscase, specification of the variant of the characteristic may allow thesystem 100 to titrate the reactions of a member of population cohort 102among various flavors of an attribute. For example, the system may beused to explore reactions of a member of population cohort 102 tovarious hairstyles of an avatar. Accordingly, a short hairstyle may bepresented, brain activity may be measured proximate to the presentationof the short hairstyle, a positive emotional reaction may be associatedwith the brain activity, and based on the positive emotional mentalstate, a body attribute specification unit 262 may specify a mediumhairstyle as an avatar attribute for subsequent presentation to themember of population cohort 102. As the process repeats, differentialreactions indicating preference, approval, and/or disapproval of themember of population cohort 102 for variations on the attribute willemerge to suggest a “most preferred attribute” for an avatar.

Accordingly, in one embodiment, an attribute specification unit 350 maysend an avatar attribute and/or other avatar output to a presentationdevice 364 for subsequent presentation to a member of population cohort102. The process may be repeated reiteratively as a means of refining anavatar to include characteristics or attributes that are linked to acohort in terms of preference or other mental state-eliciting quality.

In some embodiments, an avatar attribute may include, for example, anattribute of a three dimensional model, an attribute of a twodimensional icon or image, an attribute of a text construct, anattribute of an audio construct, and/or an attribute of a personalityconnected with a screen name. An avatar attribute may include anattribute of a real world object. In other embodiments, an avatarattribute may include, for example, an attribute of an embodiment, as ofa quality or a concept. In another example, an avatar attribute mayinclude an attribute of an archetype. In another embodiment, an avatarattribute may include a manifestation or aspect of a continuing entity,not necessarily a person. For example, a manifestation or aspect of acorporation may be an avatar attribute, as may be a specification for aclass of robots. Accordingly, a specified avatar attribute may be anoverall representation of what is happening in the population cohortwith respect to a presented characteristic. Other examples of an avatarattribute are known to those of ordinary skill in the art.

FIG. 6 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 6 illustrates example embodiments where thepresenting operation 510 may include at least one additional operation.Additional operations may include operation 600, 602, 604, 606, and/oroperation 608.

Operation 600 depicts presenting the at least one characteristic to theat least one member of a population cohort, the at least onecharacteristic associated with the at least one member of the populationcohort. For example, an attribute specification unit 150, presentationdevice 364, presentation unit 270, and/or device 106 may present atleast one characteristic to at least one member of a population cohort102, the at least one characteristic associated with the at least onemember of the population cohort 102. For example, if the member of thepopulation cohort 102 has brown hair and brown eyes, an attributespecification unit 150 may present an avatar with brown hair and browneyes to the member of population cohort 102 via presentation unit 170.

In another embodiment, a clothing specification unit 360 may specify aparticular color and/or style of clothing that matches the clothing ofthe at least one member of a population cohort 102, for presentation tothe at least one member of a population cohort 102. Similarly, a speechspecification unit may present a particular speech attribute such as alow pitch in an instance where the at least one member of a populationcohort 102 is determined to have a low-pitched voice.

Operation 602 depicts presenting the at least one characteristic to theat least one member of a population cohort, the at least onecharacteristic not associated with a member of the population cohort.For example, an attribute specification unit 150, presentation device364, presentation unit 270, and/or device 106 may present at least onecharacteristic to at least one member of a population cohort 102, the atleast one characteristic not associated with the at least one member ofthe population cohort 102. For example, if the member of the populationcohort 102 has short curly hair, an attribute specification unit 150 maypresent an avatar with long straight hair to the member of populationcohort 102 via presentation unit 170.

In another embodiment, a clothing specification unit 260 may specify aparticular color and/or style of clothing that contrasts with theclothing of the at least one member of a population cohort 102, forpresentation to the at least one member of a population cohort 102.Similarly, a speech specification unit may present a particular speechattribute such as a low pitch in an instance where the at least onemember of a population cohort 102 is determined to have a high-pitchedvoice. Such contrasting characteristic presentations may elicitinteresting mental state responses, particularly in cases wheregender-opposite characteristics (such as the voice pitch example above)are found to elicit favorable physiologic responses in the member of apopulation cohort 102.

Operation 604 depicts presenting at least one characteristic to at leastone member of a established population cohort. For example, an attributespecification unit 150, presentation device 364, presentation unit 270,and/or device 106 may present at least one characteristic to at leastone member of a established population cohort. For example, an attributespecification unit 250 may present an avatar wearing Hello Kitty brandclothing as the at least one characteristic to a member of a demographicgroup including girls ages 6-10 as the established population cohort.Alternatively, a speech specification unit 254 may present an avatarthat speaks with a British accent as the at least one characteristic toa demographic group including people living in Britain as theestablished population cohort. In another embodiment, a facial attributespecification unit 258 may present an avatar that includes a heavy beardas the at least one characteristic to a male demographic as theestablished population cohort.

Operation 606 depicts presenting the at least one characteristic to atleast one member of a established age cohort. For example, an attributespecification unit 150, presentation device 364, presentation unit 270,and/or device 106 may present at least one characteristic to at leastone member of a established age cohort. For example, an attributespecification unit 250 may present an avatar wearing Van Dutch brandclothing as the at least one characteristic to a member of a demographicgroup including women aged 18-24 as the established age cohort.Alternatively, a speech specification unit 254 may present an avatarthat speaks with a British accent as the at least one characteristic toa demographic group including British people aged 45-65 as theestablished age cohort.

In another embodiment, a facial attribute specification unit 258 maypresent an avatar that includes an approximation of Jennifer Aniston'snose as the at least one characteristic to an age demographic between18-24 years of age as the established age cohort.

Operation 608 depicts presenting the at least one characteristic to atleast one member of a established gender cohort. For example, anattribute specification unit 150, presentation device 364, presentationunit 270, and/or device 106 may present at least one characteristic toat least one member of a established gender cohort. For example, anattribute specification unit 250 may present an avatar having anOprah-like appearance as the at least one characteristic to a member ofa demographic group including women as the established gender cohort.Alternatively, a speech specification unit 254 may present an avatarthat speaks with a lilting tone as the at least one characteristic to ademographic group including men as the established gender cohort. Inanother embodiment, a body attribute specification unit 362 may presentan avatar that includes a body builder's physique as the at least onecharacteristic to at least one member of a male cohort as theestablished gender cohort.

FIG. 7 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 7 illustrates example embodiments where thepresenting operation 510 may include at least one additional operation.Additional operations may include operation 700, 702, 704, and/oroperation 706.

Operation 700 depicts presenting the at least one characteristic to atleast one member of a established ethnic cohort. For example, anattribute specification unit 150, presentation device 364, presentationunit 270, and/or device 106 may present at least one characteristic toat least one member of a established ethnic cohort, perhaps via apresentation unit 270. For example, an attribute specification unit 250may present an image including, for example, plaid clothing or a bagpipeas the at least one characteristic to a member of an ethnic groupincluding Scotsmen as the established ethnic cohort. Alternatively, aspeech specification unit 254 may present an avatar that speaks with asouthern drawl as the at least one characteristic to a member of anethnic group including inhabitants of northern states as the establishedgender cohort. In another embodiment, a facial attribute specificationunit 258 may present a sun-tanned face as the at least onecharacteristic to at least one member of a Canadian cohort as theestablished ethnic cohort.

A number of methods of identifying ethnicity based on facial featuresare known in the art, for example, ethnicity identification may beformulated as a two-category classification problem, for example, toclassify the subject as an Asian or non-Asian. The input images may beresized to different scales. At each scale, a classic appearance-basedface recognizer based on a linear discriminant analysis representationmay be developed under a Bayesian statistical decision framework. Anensemble may then be constructed by integrating classification resultsto arrive at a final decision. The product rule may be used as anintegration strategy. See Lu et al., “Ethnicity Identification from FaceImages,” Biometric Technology for Human Identification, Eds. Jain etal., Proc. SPIE, Vol. 5404, pp. 114-123 (2004).

Subject ethnicity identification may be based on a number of factorsincluding skin and/or hair characteristics associated with ethnicity,such as red hair among Caucasians; voice and/or speech associated withethnicity, such as French-accented English indicating French orFrench-Canadian ethnicity; face pattern associated with ethnicity, suchas eye shape, nose shape, face shape, or the like; and eye attributessuch as blue eyes among Caucasians. In one embodiment, Gabor waveletstransformation and retina sampling from physiologic measurement data maybe combined to extract key facial features, and support vector machinesmay be used for ethnicity classification. An experimental system hasused Gabor wavelets transformation and retina sampling in combination toextract key facial features, and support vector machines were used forethnicity classification, resulting in approximately 94% success forethnicity estimation under various lighting conditions. See Hosoi etal., “Ethnicity estimation with facial images,” Sixth IEEE InternationalConference on Automatic Face and Gesture Recognition, pp. 195-200(2004). Of course other methods of ethnicity identification known in theart may be used.

An age, gender, and/or ethnicity characteristic may be based on, forexample, an iris pattern associated with an asian user. For example, abank of multichannel 2D Gabor filters may be used to capture globaltexture information about an a user's iris, and AdaBoost, a machinelearning algorithm, may be used to allow a presentation unit 370 tolearn a discriminant classification principle from a pool of candidateiris feature sets. Iris image data may be thus grouped into racecategories, for example, Asian and non-Asian. See Qui et al., “GlobalTexture Analysis of Iris Images for Ethnic Classification,” Lecturenotes in computer science, Springer:Berlin/Heidelberg, Advances inBiometrics, pp. 411-418 (2005).

Operation 702 depicts presenting at least one of a physical appearancecharacteristic, a language characteristic, a cultural characteristic, apersonal interest characteristic, an educational characteristic, or apersonality characteristic to the at least one member of the populationcohort. For example, an attribute specification unit 150, presentationdevice 364, presentation unit 270, and/or device 106 may present atleast one of a physical appearance characteristic, a languagecharacteristic, a cultural characteristic, a personal interestcharacteristic, an educational characteristic, or a personalitycharacteristic to the at least one member of the population cohort,perhaps via a presentation unit 270. For example, an attributespecification unit 250 may present an image including, for example, atattoo as the at least one physical appearance characteristic to amember of a population cohort. Alternatively, a speech specificationunit 254 may present an avatar that speaks with a French accent as theat least one language characteristic to a member of a population cohort.In another embodiment, an attribute specification unit 258 may presentan item of sports equipment such as a tennis racquet as the at least onepersonal interest characteristic to at least one member of a populationcohort.

Operation 704 depicts presenting at least one of a facial featurecharacteristic, a body feature characteristic, a clothingcharacteristic, or an accoutrement characteristic as the at least onephysical appearance characteristic. For example, an attributespecification unit 150, clothing specification unit 260, facialattribute specification unit 258, presentation device 364, presentationunit 270, and/or device 106 may present at least one of a facial featurecharacteristic, a body feature characteristic, a clothingcharacteristic, or an accoutrement characteristic as the at least onephysical appearance characteristic, perhaps via a presentation unit 270.For example, an attribute specification unit 250 may present an imageincluding, for example, a piece of jewelry such as a ring or a necklaceas an accoutrement characteristic. Alternatively, a facial attributespecification unit 258 may present an avatar having dimples as thefacial feature characteristic. In another embodiment, a body attributespecification unit 262 may present a slender body type as the bodyfeature characteristic.

Operation 706 depicts presenting at least one of a regional dialectcharacteristic, an accent characteristic, a manner of speechcharacteristic, or a voice characteristic as the at least one languagecharacteristic. For example, an attribute specification unit 150, voicespecification unit 352, speech specification unit 354, presentationdevice 364, presentation unit 270, and/or device 106 may present atleast one of a regional dialect characteristic, an accentcharacteristic, a manner of speech characteristic, or a voicecharacteristic as the at least one language characteristic, perhaps viaa presentation unit 270. For example, a voice specification unit 252 maypresent an avatar's speech having, for example, a Boston dialect as theat least one language characteristic. Alternatively, a speechspecification unit 254 may present an avatar having an authoritativetone as the at least one language characteristic. In another embodiment,a voice specification unit 252 may present speech includingcolloquialisms such as “y'all” or “yins” as the manner of speechcharacteristic.

FIG. 8 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 8 illustrates example embodiments where thepresenting operation 510 may include at least one additional operation.Additional operations may include operation 804, and/or operation 806.

Operation 804 depicts presenting at least one of a gesturecharacteristic, a food characteristic, a hairstyle characteristic, acosmetics characteristic, or a facial hair characteristic as the atleast one cultural characteristic. For example, an attributespecification unit 150, clothing specification unit 260, non-verbalattribute specification unit 256, voice specification unit 352,presentation device 364, presentation unit 270, and/or device 106 maypresent at least one of a gesture characteristic, a food characteristic,a hairstyle characteristic, a cosmetics characteristic, or a facial haircharacteristic as the at least one cultural characteristic, perhaps viaa presentation unit 270. For example, an attribute specification unit350 may present an avatar's speech accompanied by emphatic gesturing asthe gesture characteristic. Alternatively, a non-verbal attributespecification unit 356 may present an avatar wearing traditional dressof an ethnicity as the at least one cultural characteristic. In anotherembodiment, a facial attribute specification unit 358 may present facialhair such as a mustache or a goatee as the facial hair characteristic.

Operation 806 depicts presenting at least one of a hobby interestcharacteristic, a travel interest characteristic, a shopping interestcharacteristic, a pet interest characteristic, a television or movieinterest characteristic, a music interest characteristic, a politicsinterest characteristic, or a sports interest characteristic as the atleast one personal interest characteristic. For example, an attributespecification unit 150, clothing specification unit 260, non-verbalattribute specification unit 256, voice specification unit 352,presentation device 364, presentation unit 270, and/or device 106 maypresent at least one of a hobby interest characteristic, a travelinterest characteristic, a shopping interest characteristic, a petinterest characteristic, a television or movie interest characteristic,a music interest characteristic, a politics interest characteristic, ora sports interest characteristic as the at least one personal interestcharacteristic, perhaps via a presentation unit 270. For example, anattribute specification unit 350 may present an avatar together with amusical instrument as the music interest characteristic. Alternatively,a non-verbal attribute specification unit 356 may present an avatarwearing a Star Trek t-shirt as the television or movie interestcharacteristic. In another embodiment, a facial attribute specificationunit 358 may present an avatar's face painted with a sports team'scolors as the sports interest characteristic.

FIG. 9 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 9 illustrates example embodiments where thepresenting operation 510 may include at least one additional operation.Additional operations may include operation 904, and/or operation 906.

Operation 904 depicts presenting at least one of a literature interestcharacteristic, a science interest characteristic, a mathematicsinterest characteristic, or a college or university characteristic asthe at least one educational interest characteristic. For example, anattribute specification unit 150, clothing specification unit 260,non-verbal attribute specification unit 256, voice specification unit352, presentation device 364, presentation unit 270, and/or device 106may present at least one of a literature interest characteristic, ascience interest characteristic, a mathematics interest characteristic,or a college or university characteristic as the at least oneeducational interest characteristic, perhaps via a presentation unit270. For example, an attribute specification unit 350 may present anavatar together with a book as the literature interest characteristic.Alternatively, a non-verbal attribute specification unit 356 may presentan avatar wearing a computer as the science interest characteristic. Inanother embodiment, a clothing specification unit 260 may present anavatar wearing the logo of a school as the college or universitycharacteristic.

Operation 906 depicts presenting at least one of a talkative personalitycharacteristic, an aggressive personality characteristic, a deferentialpersonality characteristic, a humorous personality characteristic, or awhimsical personality characteristic as the at least one personalitycharacteristic. For example, an attribute specification unit 150,clothing specification unit 260, non-verbal attribute specification unit256, voice specification unit 352, presentation device 364, presentationunit 270, and/or device 106 may present at least one of a talkativepersonality characteristic, an aggressive personality characteristic, adeferential personality characteristic, a humorous personalitycharacteristic, or a whimsical personality characteristic as the atleast one personality characteristic, perhaps via a presentation unit270. For example, an attribute specification unit 350 may present anavatar that rarely makes eye contact as the deferential personalitycharacteristic. Alternatively, a speech specification unit 354 maypresent an avatar that talks often and at a rapid rate as the talkativepersonality characteristic. In another embodiment, a facial attributespecification unit 358 may present an avatar that smiles and or laughsfrequently as the humorous personality characteristic.

FIG. 10 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 10 illustrates example embodiments where thepresenting operation 510 may include at least one additional operation.Additional operations may include operation 1000, and/or operation 1002.

Operation 1000 depicts presenting at least one characteristic to atleast one of a virtual world participant, a computer game participant,an online shopping participant, or a social networking websiteparticipant as the at least one member of the population cohort. Forexample, an attribute specification unit 150, clothing specificationunit 260, non-verbal attribute specification unit 256, voicespecification unit 352, presentation device 364, presentation unit 270,and/or device 106 may present at least one characteristic to at leastone of a virtual world participant, a computer game participant, anonline shopping participant, or a social networking website participantas the at least one member of the population cohort, perhaps via apresentation unit 270. For example, an attribute specification unit 350may present an avatar to a Second Life user as the virtual worldparticipant. Alternatively, a non-verbal attribute specification unit256 may present an avatar to a player of an online game, such as Worldof Warcraft as the computer game participant. In another embodiment, afacial attribute specification unit 358 may present an avatar to aperson visiting a myspace page or a facebook page as the socialnetworking website participant.

Operation 1002 depicts presenting at least two characteristics separatedby a time delay to the at least one member of a population cohort. Forexample, an attribute specification unit 150, clothing specificationunit 260, non-verbal attribute specification unit 256, voicespecification unit 352, presentation device 364, presentation unit 270,and/or device 106 may present at least two characteristics separated bya time delay to the at least one member of a population cohort, perhapsvia a presentation unit 270. For example, an attribute specificationunit 350 may present an avatar having a particular advertising logodisplayed on an item of clothing, followed some time later by anotheravatar having a different logo displayed on the same avatar. Thusphysiologic responses to the different logos may be measured andcompared. Alternatively, a non-verbal attribute specification unit 256may present an avatar having a particular physical feature such as blueeye color, followed some time later by an avatar the same in allrespects but for a change to green eye color. Thus physiologic responsesto the different eye colors may be measured and compared.

FIG. 11 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 11 illustrates example embodiments where themeasuring operation 520 may include at least one additional operation.Additional operations may include operation 1100, 1102, and/or operation1104.

Operation 1100 depicts measuring at least one physiologic activity ofthe at least one member of the population cohort using functionalnear-infrared imaging, the at least one physiologic activity proximateto the at least one presented characteristic. For example, the device206, physiologic activity measurement unit 210, brain activitymeasurement unit 212, and/or fNIR module 214 can measure at least onephysiologic activity of the at least one member of the population cohortusing functional near-infrared imaging, the at least one physiologicactivity proximate to the at least one presented characteristic. In oneembodiment, a member of the population cohort 102 can be monitored byFNIR module 214 during presentation of a characteristic to a member ofpopulation cohort 102. fNIR module 214 can measure brain activity as thephysiologic activity of the at least one member of the populationcohort.

Proximity of the fNIR measurement to presentation, viewing, and/orperception of the characteristic can be determined by presentation unit270, device 206, physiologic activity measurement unit 210, brainactivity measurement unit 212, and/or FNIR module 214. For example, thetime of presentation of the characteristic can be matched with brainactivity measured by fNIR module 214. In another embodiment, eyemovement and/or gaze tracking analysis can determine the time that asubject's eyes contact a presented characteristic, and this can bematched to the time of a measured brain activity by FNIR module 214. Instill another embodiment, brain activity in the visual cortex or otherperception-indicative brain area or areas can be measured by FNIR module214 as an indicator of a subject's viewing of a presentedcharacteristic; continued measurement of physiologic activity by fNIRmodule 214 can then measure a response to the viewing of thecharacteristic.

In one embodiment, the fNIR module 214 may be located in a kiosk in apublic area such as a shopping mall. In such an environment, images ofindividuals may be captured by photography or videography and comparedwith reference population cohort 104 image profiles to identify membersof population cohort 102. In another embodiment, presentation of acharacteristic by, for example, presentation unit 270 and measurement ofphysiologic activity by, for example fNIR module 214 may occur in a homecomputing environment, with characteristic presentation data sent by,for example, a remote attribute specification unit 350 to a presentationdevice 364 located in the home. The physiologic activity measurementunit 310 unit may be located in the home environment and sendmeasurement data via a network to a remote device 306 for processing ofthe data, or all functions of device 106 may be located in the homeenvironment.

In one embodiment, fNIR module 214 may measure brain activation withinmilliseconds of a subject encountering a presented characteristic. Forexample, fNIR module 214 may detect increased brain activity in thenucleus accumbens, SLEA, and thalamus within milliseconds ofpresentation of a prepared food item to a member of population cohort102. Such a response may be considered proximate to the presentation ofthe prepared food item.

Operation 1102 depicts measuring at least one physiologic activity ofthe at least one member of the population cohort using at least one ofelectroencephalography, computed axial tomography, positron emissiontomography, magnetic resonance imaging, functional magnetic resonanceimaging, functional near-infrared imaging, or magnetoencephalography,the at least one physiologic activity proximate to the at least onepresented characteristic. For example, the device 206, physiologicactivity measurement unit 210, brain activity measurement unit 212, fMRImodule 216, MEG module 218, EEG module, PET module, and/or FNIR module214 can measure at least one physiologic activity of the at least onemember of the population cohort using at least one ofelectroencephalography, computed axial tomography, positron emissiontomography, magnetic resonance imaging, functional magnetic resonanceimaging, functional near-infrared imaging, or magnetoencephalography,the at least one physiologic activity proximate to the at least onepresented characteristic. In one embodiment, a member of the populationcohort 102 can be monitored by MEG module 218 during presentation of acharacteristic to a member of population cohort 102. fMRI module 216,MEG module 218, EEG module, PET module, and/or fNIR module 214 canmeasure brain activity as the physiologic activity of the at least onemember of the population cohort.

Operation 1104 depicts measuring at least one brain activity surrogatemarker of the at least one member of the population cohort, the at leastone brain activity surrogate marker proximate to the at least onepresented characteristic. For example, the device 206, physiologicactivity measurement unit 210, surrogate marker measurement unit 230,iris response module 232, gaze tracking module 234, skin response module236, and/or voice response module can measure at least one brainactivity surrogate marker of the at least one member of the populationcohort, the at least one brain activity surrogate marker proximate tothe at least one presented characteristic. In one embodiment, surrogatemarker measurement unit 230 can measure any of a variety ofphysiological responses to a presented characteristic, the physiologicalresponses indicative of a brain activation and/or mental state responseto the presented characteristic. For example, surrogate markermeasurement unit 230 may measure a physiological attribute such as heartrate, respiration, perspiration, temperature, skin coloring, skinelectrical response, eye movement, pupil dilation, voice stress, body orfacial tic, or the like, before, during, and/or after presentation of acharacteristic to a member of population cohort 102. Such measurementsmay include, for example, an increase in heart rate over a time intervalas measured by a heart rate monitor embedded in a user interface;increased eye movements as measured by an image capture device such as avideo camera, or changes in the galvanic skin response as measured byelectrodes embedded in a user interface or included in device 106.

In one embodiment, an iris response module 232 and/or gaze trackingmodule 234 may acquire data from a user monitoring device such as avideo capture device or a video communication device, for example, whena subject's image is captured as a photograph or video when using anapplication, such as a webcam, or when a subject's image is capturedwhen communicating via a photography or video-based application. Othersources of image data may include biometric data such as facial patterndata, eye scanning data, or the like. Such image data may indicate, forexample, alertness, attention, approval, disapproval, or the like, asdiscussed below.

Image data may include results of visual spectrum imaging that can imagechanges in facial expression, body movement, or the like that can beindicative of a physiological activity, brain activity, and/or mentalstate. User image data may also be obtained from other kinds of imagingsuch as infrared imaging that can read a heat signature and/orultrasound imaging. Further, reflected image or refracted image data mayalso be obtained by physiologic activity measurement unit 210 and/orsurrogate marker measurement unit 230. Near infrared imaging may be usedto test for baseline physiologic states and metabolism, as well asphysiologic and metabolic changes. Image data may be of all or a portionof a member of population cohort 102 such as a head-to-toe image, a faceimage, an image of fingers, an image of an eye, or the like. Such imagesmay be in the visual or non-visual wavelength range of theelectromagnetic spectrum.

Alertness or attention can be measured, for example, by measuring eyemovements, body movements, pointing device manipulation, and/or taskproficiency (e.g., are a subject's eyelids drooping, is a subject's headnodding, is a subject failing or succeeding to activate on-screen itemswhen prompted, does a subject respond to a sound, or the like).Alertness or attention to, for example, an advertisement may be gaugedfrom a subject's interaction with the advertisement. Interest in theadvertisement as the presented characteristic in the form of facepattern data (e.g., a smile on an image of the subject's face), pointingdevice manipulation data (e.g., a mouse click on an onscreenadvertisement icon as the presented characteristic), and/or eye movementdata (e.g., repeated eye movements toward the advertisement as thepresented characteristic), or the like.

FIG. 12 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 12 illustrates example embodiments where themeasuring operation 520 may include at least one additional operation.Additional operations may include operation 1206.

Operation 1206 depicts measuring the at least one brain activitysurrogate marker of the at least one member of the population cohortusing at least one of iris dilation or constriction, gaze tracking, skinresponse, or voice response, the at least one brain activity surrogatemarker proximate to the at least one characteristic. For example, thedevice 206, physiologic activity measurement unit 210, surrogate markermeasurement unit 230, iris response module 232, gaze tracking module234, skin response module 236, and/or voice response module can measurethe at least one brain activity surrogate marker of the at least onemember of the population cohort using at least one of iris dilation orconstriction, gaze tracking, skin response, or voice response, the atleast one brain activity surrogate marker proximate to the at least onepresented characteristic. In one embodiment, a voice response module canmeasure speech captured by a microphone during presentation of acharacteristic. Speech or voice can be measured, for example, byexamining voice, song, and/or other vocal utterances of a subjectbefore, during, and/or after presentation of a characteristic to amember of population cohort 102. Such measurements may include, forexample, as discussed above, layered voice analysis, voice stressanalysis, or the like.

The reaction of a subject to a presented characteristic such as anadvertisement in a computerized game world or in another virtual worldmay be a recognizable vocal exclamation such as “Wow, that's nice!” thatmay be detectable by a voice response module 338 such as a microphonemonitoring an interaction between the subject and, for example, apresentation device 364 and/or device 306. A voice response module 338may also include a speech recognition function such as a softwareprogram or computational device that can identify and/or record anutterance of a subject as speech or voice data.

In another embodiment, an iris response module may record changes in themovement of a subject's iris (with corresponding changes in the size ofthe pupil) before, during, and/or after presentation of a characteristicto a member of a population cohort 102. Such measurements of physiologicactivity that indicate brain activity and/or mental state may be carriedout at a time that is proximate to presentation of a characteristic to amember of population cohort 102.

In one embodiment, a gaze tracking module 334 may include a camera thatcan monitor a subject's eye movements in order to determine whether thesubject looks at a presented characteristic, for example, during acertain time period.

Gaze tracking module 334 and/or iris response module 332 may include asmart camera that can capture images, process them and issue controlcommands within a millisecond time frame. Such smart cameras arecommercially available (e.g., Hamamatsu's Intelligent Vision System;http://ip.hamamatsu.com/en/product_info/index.html). Such image capturesystems may include dedicated processing elements for each pixel imagesensor. Other camera systems may include, for example, a pair ofinfrared charge coupled device cameras to continuously monitor pupilsize and position as a user watches a visual target moving forward andbackward. This can provide real-time data relating to pupilaccommodation relative to objects on, for example, a display 372. (e.g.,http://ip.hamamatsu.com/en/rd/publication/scientific_american/common/pdf/scientific_(—)0608.pdf).

Eye movement and/or iris movement may also be measured by video-basedeye trackers. In these systems, a camera focuses on one or both eyes andrecords eye movement as the viewer looks at a stimulus. Contrast may beused to locate the center of the pupil, and infrared and near-infrarednon-collumnated light may be used to create a corneal reflection. Thevector between these two features can be used to compute gazeintersection with a surface after a calibration for an user 106.

Two types of gaze tracking or eye tracking techniques include brightpupil eye tracking and dark pupil eye tracking. Their difference isbased on the location of the illumination source with respect to theoptics. If the illumination is coaxial with the optical path, then theeye acts as a retroreflector as the light reflects off the retina,creating a bright pupil effect similar to red eye. If the illuminationsource is offset from the optical path, then the pupil appears dark.

Bright Pupil tracking creates greater iris/pupil contrast allowing formore robust eye tracking with all iris pigmentation and greatly reducesinterference caused by eyelashes and other obscuring features. It alsoallows for tracking in lighting conditions ranging from total darknessto very bright light. However, bright pupil techniques are notrecommended for tracking outdoors as extraneous IR sources may interferewith monitoring.

Eye tracking configurations can vary; in some cases the measurementapparatus may be head-mounted, in some cases the head should be stable(e.g., stabilized with a chin rest), and in some cases the eye trackingmay be done remotely to automatically track the head during motion. Mosteye tracking systems use a sampling rate of at least 30 Hz. Although50/60 Hz is most common, many video-based eye trackers run at 240, 350or even 1000/1250 Hz, which is recommended in order to capture thedetail of the very rapid eye movements during reading, or during studiesof neurology.

Eye movements are typically divided into fixations, when the eye gazepauses in a certain position, and saccades, when the eye gaze moves toanother position. A series of fixations and saccades is called ascanpath. Most information from the eye is made available during afixation, not during a saccade. The central one or two degrees of thevisual angle (the fovea) provide the bulk of visual information; inputfrom larger eccentricities (the periphery) generally is lessinformative. Therefore the locations of fixations along a scanpathindicate what information loci on the stimulus were processed during aneye tracking session. On average, fixations last for around 200milliseconds during the reading of linguistic text, and 350 millisecondsduring the viewing of a scene. Preparing a saccade towards a new goaltakes around 200 milliseconds.

Scanpaths are useful for analyzing cognitive intent, interest, andsalience. Other biological factors (some as simple as gender) may affectthe scanpath as well. Eye tracking in human-computer interactiontypically investigates the scanpath for usability purposes, or as amethod of input in gaze-contingent displays, also known as gaze-basedinterfaces.

There are two primary components to most eye tracking studies:statistical analysis and graphic rendering. These are both based mainlyon eye fixations on specific elements. Statistical analyses generallysum the number of eye data observations that fall in a particularregion. Commercial software packages may analyze eye tracking and showthe relative probability of eye fixation on each feature in a website.This allows for a broad analysis of which site elements receivedattention and which ones were ignored. Other behaviors such as blinks,saccades, and cognitive engagement can be reported by commercialsoftware packages. Statistical comparisons can be made to test, forexample, competitors, prototypes or subtle changes to an avatar and/orweb design. They can also be used to compare participants in differentdemographic groups and/or population cohorts. Statistical analyses mayquantify where subjects look, sometimes directly, and sometimes based onmodels of higher-order phenomena (e.g., cognitive engagement).

In addition to statistical analysis, it is often useful to providevisual depictions of eye tracking results. One method is to create avideo of an eye tracking testing session with the gaze of a participantsuperimposed upon it. This allows one to effectively see through theeyes of the subject during interaction with a presented characteristic.Another method graphically depicts the scanpath of a single participantduring a given time interval. Analysis may show each fixation and eyemovement of a participant during a search on a virtual shelf display ofbreakfast cereals, analyzed and rendered with a commercial softwarepackage. For example, a different color may represent one second ofviewing time, allowing for a determination of the order in whichproducts are seen. Analyses such as these may be used as evidence ofspecific trends in visual behavior.

A similar method sums the eye data of multiple participants during agiven time interval as a heat map. A heat map may be produced by acommercial software package, and shows the density of eye fixations forseveral participants superimposed on the original stimulus, for example,a magazine cover. Red and orange spots represent areas with highdensities of eye fixations. This allows one to examine which regionsattract the attention and focus of the viewer.

Commercial eye tracking applications include web usability, advertising,sponsorship, package design and automotive engineering. Eye trackingstudies often present a target stimulus to a sample of consumers whilean eye tracker is used to record the activity of the eye. Examples oftarget stimuli may include websites, television programs, sportingevents, films, commercials, magazines, newspapers, packages, shelfdisplays, consumer systems (e.g., ATMs, checkout systems, kiosks), andsoftware. The resulting data can be statistically analyzed andgraphically rendered to provide evidence of specific visual patterns. Byexamining fixations, saccades, pupil dilation, blinks, and a variety ofother behaviors, researchers can determine a great deal about theeffectiveness of a given medium or product.

A prominent field of eye tracking research is web usability. Whiletraditional usability techniques are often quite powerful in providinginformation on clicking and scrolling patterns, eye tracking offers theability to analyze user interaction between the clicks. This providesinsight into which features are the most eye-catching, which featurescause confusion, and which ones are ignored altogether. Specifically,eye tracking can be used to assess search efficiency, branding, onlineadvertisement, navigation usability, overall design, and many other sitecomponents, including avatar attributes. Analyses may target a prototypeavatar or competitor avatars in addition to the main avatar.

Eye tracking is commonly used in a variety of different advertisingmedia. Commercials, print ads, online ads, and sponsored programs areall conducive to analysis with eye tracking technology. Analyses mayfocus on visibility of a target product or logo in the context of amagazine, newspaper, website, virtual world, or televised event. Thisallows researchers to assess in great detail how often a sample ofconsumers fixates on the target logo, product, or advertisement. In thisway, an advertiser can quantify the success of a given campaign in termsof actual visual attention.

Eye tracking also provides package designers with the opportunity toexamine the visual behavior of a consumer while interacting with atarget package. This may be used to analyze distinctiveness,attractiveness and the tendency of the package to be chosen forpurchase. Eye tracking is often used while the target product is in theprototype stage. Prototypes are tested against each other and againstcompetitors to examine which specific elements are associated with highvisibility and/or appeal.

Another application of eye tracking research is in the field ofautomotive design. Eye tracking cameras may be integrated intoautomobiles to provide the vehicle with the capacity to assess inreal-time the visual behavior of the driver. The National HighwayTraffic Safety Administration (NHTSA) estimates that drowsiness is theprimary causal factor in 100,000 police-reported accidents per year.Another NHTSA study suggests that 80% of collisions occur within threeseconds of a distraction. By equipping automobiles with the ability tomonitor drowsiness, inattention, and cognitive engagement driving safetycould be dramatically enhanced. Lexus® claims to have equipped its LS460 automobile with the first driver monitor system in 2006, providing awarning if the driver takes his or her eye off the road.

Eye tracking is also used in communication systems for disabled persons,allowing the user to speak, mail, surf the web and so with only the eyesas tool. Eye control works even when the user has involuntary bodymovement as a result of cerebral palsy or other disability, and/or whenthe user wears glasses.

A surrogate marker measurement unit may also measure face pattern, forexample, by measuring user facial features, perhaps in relation to acontrol user face pattern image captured when the user was notinteracting with a presented characteristic. Alternatively, a subject'sface pattern may be compared to an average face pattern compiled from alarge number of faces in the population cohort. In one embodiment, thereaction of a member of population cohort 102 to an onscreen avatar maybe a smile or a frown that may be detectable by a camera monitoring theinteraction. Information suggesting that a user smiles in response toviewing a presented characteristic may be transmitted to associationunit 340.

A surrogate marker measurement unit 330 may include a pen includingelectronic sensing capability that may include the capability to monitora subject's hand for temperature, blood flow, tremor, fingerprints, orother attributes. Reaction time among young males aged 18-28 that playvideo games with some frequency may be distinguishable from averagereaction times of women and/or users in older age groups.

In another embodiment, a surrogate marker measurement unit 330 and/ordevice 306 may anonymize physiologic activity measurement data acquiredduring a subject's interaction with one or more presentedcharacteristics. Anonymization of member of population cohort 102 datamay be accomplished through various methods known in the art, includingdata coding, k-anonymization, de-association, pseudonymization, or thelike.

FIG. 13 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 13 illustrates example embodiments where themeasuring operation 520 may include at least one additional operation.Additional operations may include operation 1300, 1302, 1304, and/or1306.

Operation 1300 depicts measuring at least one physiologic activity ofthe at least one member of the population cohort in near real time, theat least one physiologic activity proximate to the at least onepresented characteristic. For example, the device 206, physiologicactivity measurement unit 210, brain activity measurement unit 212,and/or surrogate marker measurement unit 230 can measure at least onephysiologic activity of the at least one member of the population cohort102 in near real time, the at least one physiologic activity proximateto the at least one presented characteristic. In one embodiment, thebrain activity measurement unit 212 can measure brain activity in themember of population cohort 102 at the milliseconds-to-seconds timeframe, inclusive of processing time. Accordingly, the methods discussedherein, including surrogate marker measurement functions, can measureresponses of a member of the population cohort 102 in near real time,which may include a delay between the occurrence of a proximate and/orresponse event and the use of the processed proximate measurement data,e.g., for further processing and/or for subsequent display.

Operation 1302 depicts measuring at least one physiologic activity ofthe at least one member of the population cohort, the at least onephysiologic activity proximate to at least two presentedcharacteristics. For example, the device 206, physiologic activitymeasurement unit 210, brain activity measurement unit 212, and/orsurrogate marker measurement unit 230 can measure at least onephysiologic activity of the at least one member of the populationcohort, the at least one physiologic activity proximate to at least twopresented characteristics. In one embodiment, a combination ofcharacteristics can be presented to a member of population cohort 102 inthe context of, for example an avatar. For example, brain activitymeasurement unit 212 may measure brain activity during presentation of aset of avatar features, such as a facial feature and a voice feature, toa member of population cohort 102.

Operation 1304 depicts measuring at least one physiologic activity ofthe at least one member of the population cohort, the at least onephysiologic activity proximate to at least one of a presented hairstylecharacteristic or a presented eye color characteristic. For example, thedevice 206, physiologic activity measurement unit 210, brain activitymeasurement unit 212, and/or surrogate marker measurement unit 230 canmeasure at least one physiologic activity of the at least one member ofthe population cohort, the at least one physiologic activity proximateto at least one of a presented hairstyle characteristic or a presentedeye color characteristic. In one embodiment, physiologic activitymeasurement unit 210 can measure brain activity and/or eye movementactivity during presentation of an avatar to a member of populationcohort 102, the avatar having, for example, wavy blond hair and/or blueeyes. Choice of hairstyle characteristic may be based on a populationcohort 104 characteristic, a member of population cohort 102characteristic, and/or previous avatar specification data.

Operation 1306 depicts measuring at least one physiologic activity ofthe at least one member of the population cohort, the at least onephysiologic activity proximate to at least one presented characteristicof the member of the population cohort. For example, the device 206,physiologic activity measurement unit 210, brain activity measurementunit 212, and/or surrogate marker measurement unit 230 can measure atleast one physiologic activity of the at least one member of thepopulation cohort, the at least one physiologic activity proximate to atleast one presented characteristic of the member of the populationcohort. In one embodiment, physiologic activity measurement unit 210 canmeasure brain activity and/or iris movement activity during presentationof an appearance characteristic of the member of the population cohort102, for example, as detected by a camera monitoring the member of thepopulation cohort. In another embodiment, physiologic activitymeasurement unit 210 can measure brain activity and/or voice stressresponse during presentation of a voice characteristic of the member ofthe population cohort 102, for example, as detected by a microphonemonitoring the member of the population cohort.

FIG. 14 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 14 illustrates example embodiments where themeasuring operation 520 may include at least one additional operation.Additional operations may include operation 1400, 1402, 1404, and/or1406.

Operation 1400 depicts measuring at least one physiologic activity ofthe at least one member of the population cohort, the at least onephysiologic activity proximate to at least one presented characteristicof a different member of the population cohort. For example, the device206, physiologic activity measurement unit 210, brain activitymeasurement unit 212, and/or surrogate marker measurement unit 230 canmeasure at least one physiologic activity of the at least one member ofthe population cohort, the at least one physiologic activity proximateto at least one presented characteristic of a different member of thepopulation cohort 104. In one embodiment, brain activity measurementunit 312 can measure brain activity during presentation of an appearancecharacteristic of a member of population cohort 104 different from themember of population cohort 102 that is responding to thecharacteristic. For example, a middle-aged member of population cohort102 may be presented with a characteristic of a child-aged member of thepopulation cohort 104, during which physiologic activity measurementunit 210, brain activity measurement unit 212, and/or surrogate markermeasurement unit 230 can measure a physiological response to thepresentation of the characteristic.

Operation 1402 depicts measuring at least one physiologic activity ofthe at least one member of the population cohort, the at least onephysiologic activity proximate to at least one presented characteristicof a member of a different population cohort. For example, the device206, physiologic activity measurement unit 210, brain activitymeasurement unit 212, and/or surrogate marker measurement unit 230 canmeasure at least one physiologic activity of the at least one member ofthe population cohort, the at least one physiologic activity proximateto at least one presented characteristic of a member of a differentpopulation cohort. In one embodiment, brain activity measurement unit312 can measure brain activity during presentation of a speechcharacteristic of a member of a population cohort that is different fromthe population cohort 104 to which member of population cohort 102belongs. For example, for a population cohort 104 including males aged18-24, a member of population cohort 102 (i.e., a male aged 18-24) maybe presented with a characteristic of a female, and thus a member of adifferent population cohort, during which physiologic activitymeasurement unit 210, brain activity measurement unit 212, and/orsurrogate marker measurement unit 230 can measure a physiologicalresponse to the presentation of the female characteristic.

Operation 1404 depicts measuring with permission at least onephysiologic activity of the at least one member of the populationcohort, the at least one physiologic activity proximate to the at leastone presented characteristic. For example, the device 206, physiologicactivity measurement unit 210, brain activity measurement unit 212,and/or surrogate marker measurement unit 230 can measure with permissionat least one physiologic activity of the at least one member of thepopulation cohort, the at least one physiologic activity proximate tothe at least one presented characteristic. In one embodiment, afterreceiving permission from a member of population cohort 102, brainactivity measurement unit 312 can measure brain activity duringpresentation of a physical characteristic to the member of populationcohort 102. For example, a member of population cohort 102 may click anacceptance box asking for permission to measure a physiologic activity,for example, during a virtual world session, an online gaming session,an online shopping session, a social networking session, or the like.

FIG. 15 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 15 illustrates example embodiments where theassociating operation 530 may include at least one additional operation.Additional operations may include operation 1500, 1502, 1504, 1506,and/or 1508.

Operation 1500 depicts associating at least one electrical brainactivity with the at least one mental state. For example, the device106, association unit 140, emotion association module 242, attentionassociation module 244, and/or cognition association module 346 canassociate at least one electrical brain activity with at least onemental state. In one embodiment, association unit 240 can receive anelectrical brain activity measurement, such as measurement of electricalactivation of the hippocampus, in response to presentation of acharacteristic to a member of population cohort 102. The electricalbrain activity measurement may be received from, for example, EEG module220. Association unit 240 can then search one or more functional brainmapping databases based on the electrical brain activity measurement tofind one or more matching mental states. For example, activation of thehippocampus is associated in the literature with enhanced motivationaldrive for food and drugs. Thus an association may be made betweenhippocampus activation and enhanced motivational drive, for example,based on research findings (e.g., Wang et al., “Gastric stimulation inobese subjects activates the hippocampus and other regions involved inbrain reward circuitry,” PNAS, vol. 103, pp. 15641-45 (2006).

In another embodiment, attention association module 244 can receive abrain activity measurement based on electrical brain activity, such as ameasurement from MEG module 218. In this embodiment, an avatar with aspecific set of facial features as the at least one characteristic maybe presented to a member of population cohort 102, during whichpresentation MEG module 218 can measure the brain's electrical activitybased on indicated magnetic fields. For example, presentation of theavatar with the specific set of facial features may elicit electricalactivity in the prefrontal and/or parietal areas of the brain.Association unit 240 may thus match the activation measurement patternwith a known pattern of brain activation from research indicating whichbrain areas are activated when attention is required. For example,activation of the thalamic reticular nucleus is also associated withselective attention. See Contreras et al., “Inactivation of theInteroceptive Insula Disrupts Drug Craving and Malaise Indicated byLithium,” Science, vol. 318, pp. 655-658 (26 Oct. 2007).

Operation 1502 depicts associating at least one brain blood oxygen levelwith the at least one mental state. For example, the device 106,association unit 140, emotion association module 242, attentionassociation module 244, and/or cognition association module 346 canassociate at least one hemodynamic brain activity with at least onemental state. In one embodiment, association unit 240 can receive abrain blood oxygen level activity measurement, such as measurement ofactivation of the right prefrontal and parietal areas, in response topresentation of a characteristic to a member of population cohort 102.The hemodynamic brain activity measurement indicating activation of theright prefrontal and parietal areas may be received from, for example,fNIR module 214. Association unit 240 can then search one or morefunctional brain mapping databases based on the brain activitymeasurement to find one or more matching mental states. For example,activation of the right prefrontal and parietal areas is associated inthe literature with attention. Thus an association may be made betweenright prefrontal and parietal areas activation and increased attentionto the presented characteristic by association unit 240, for example,based on research findings. See Cabeza et al, “Imaging Cognition II: AnEmpirical Review of 275 PET and fMRI Studies,” J. Cognitive Neurosci.,vol. 12, pp. 1-47 (2000).

Operation 1504 depicts associating at least one of glucose metabolism orblood flow with the at least one mental state. For example, the device106, association unit 140, emotion association module 242, attentionassociation module 244, and/or cognition association module 346 canassociate at least one of glucose metabolism or blood flow with at leastone mental state. In one embodiment, association unit 240 can receive aglucose metabolism measurement, such as measurement of activation of theright hemisphere of the bilateral fusiform gyrus, in response topresentation of a face characteristic to a member of population cohort102. The glucose metabolism brain activity measurement indicatingactivation of the right hemisphere of the bilateral fusiform gyrus maybe received from, for example, PET module 222. Attention associationmodule 244 can then match the brain activity measurement to one or morecorresponding mental states. For example, activation of the righthemisphere of the bilateral fusiform gyrus is associated in theliterature with increased attention to faces. Thus an association can bemade between activation of the right hemisphere of the bilateralfusiform gyrus and increased attention to the presented facecharacteristic by association unit 240, for example, based on researchfindings. See Cabeza et al, “Imaging Cognition II: An Empirical Reviewof 275 PET and fMRI Studies,” J. Cognitive Neurosci., vol. 12, pp. 1-47(2000).

Operation 1506 depicts associating activity in the ventromedialprefrontal cortex with a mental state indicating approval. For example,the device 106, association unit 140, emotion association module 242,attention association module 244, and/or cognition association module346 can associate activity in the ventromedial prefrontal cortex with amental state indicating approval. In one embodiment, association unit240 can receive a brain activity measurement indicating activation ofthe ventromedial prefrontal cortex, in response to presentation to amember of population cohort 102 of an avatar having a characteristic ofinterest. The brain activity measurement indicating activation of theventromedial prefrontal cortex may be received from, for example, fMRImodule 216. Emotion association module 242 and/or attention associationmodule 244 can then match the ventromedial prefrontal cortex activationmeasurement to one or more corresponding mental states. For example,activation of the ventromedial prefrontal cortex is associated in theliterature with preference or approval. Thus an association unit 240 canmake an association between activation of the ventromedial prefrontalcortex and approval of the presented avatar having the characteristic ofinterest, for example, based on research findings. See Cabeza et al,“Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies,”J. Cognitive Neurosci., vol. 12, pp. 1-47 (2000).

Operation 1508 depicts associating activity in at least one of thefrontopolar cortex, the prefrontal cortex, the ventral striatum, theorbitofrontal prefrontal cortex, the amygdala, or the nucleus accumbenswith a mental state indicating approval. For example, the device 106,association unit 140, emotion association module 242, attentionassociation module 244, and/or cognition association module 346 canassociate activity in at least one of the frontopolar cortex, theprefrontal cortex, the ventral striatum, the orbitofrontal prefrontalcortex, the amygdala, or the nucleus accumbens with a mental stateindicating approval. In one embodiment, association unit 240 can receivea brain activity measurement indicating activation of the nucleusaccumbens in response to presentation to a member of population cohort102 of an avatar possessing a product as the at least onecharacteristic. The brain activity measurement indicating activation ofthe nucleus accumbens may be received from, for example, MEG module 218.Emotion association module 242 and/or attention association module 244can then match the nucleus accumbens activation measurement to one ormore corresponding mental states. For example, activation of the nucleusaccumbens is associated in the literature with product preference. Thusan association can be made between activation of the nucleus accumbensand preference for the presented avatar possessing a product, byassociation unit 240, for example, based on research findings. See Wise,“Thought Police: How Brain Scans Could Invade Your Private Life,”Popular Mechanics, (November 2007).

FIG. 16 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 16 illustrates example embodiments where theassociating operation 530 may include at least one additional operation.Additional operations may include operation 1600, 1602, 1604, 1606,and/or 1608.

Operation 1600 depicts associating activity in the prefrontal cortexwith a mental state indicating brand preference. For example, the device106, association unit 140, emotion association module 242, attentionassociation module 244, and/or cognition association module 346 canassociate activity in the prefrontal cortex with a mental stateindicating brand preference. In one embodiment, association unit 240 canreceive a brain activity measurement indicating activation of theprefrontal cortex in response to presentation to a member of populationcohort 102 of an image of a product brand as the at least oncharacteristic. The brain activity measurement indicating activation ofthe prefrontal cortex may be received from, for example, fNIR module314. Emotion association module 242, attention association module 244,and/or cognition association module 346 can then match the prefrontalcortex activation measurement to one or more corresponding mentalstates. For example, activation of the prefrontal cortex is associatedin the literature with brand preference. Thus an association can be madebetween activation of the prefrontal cortex and preference for thepresented image of a product brand, by association unit 240, forexample, based on research findings. See Kenning et al.,“Neuroeconomics: an overview from an economic perspective,” Brain Res.Bull., vol. 67, pp. 343-354 (2005).

Operation 1602 depicts associating activity in the dorsolateralprefrontal cortex, the posterior parietal cortex, the occipital cortex,and the left premotor area with a mental state indicating brandpreference. For example, the device 106, association unit 140, emotionassociation module 242, attention association module 244, and/orcognition association module 346 can associate activity in thedorsolateral prefrontal cortex, the posterior parietal cortex, theoccipital cortex, and the left premotor area with a mental stateindicating brand preference. In one embodiment, association unit 240 canreceive a brain activity measurement indicating activation of thedorsolateral prefrontal cortex, the posterior parietal cortex, theoccipital cortex, and the left premotor area in response to presentationto a member of population cohort 102 of a brand embodied in an avatar asthe at least on characteristic. The brain activity measurementindicating activation of the dorsolateral prefrontal cortex, theposterior parietal cortex, the occipital cortex, and the left premotorarea may be received from, for example, FNIR module 314. Emotionassociation module 242, attention association module 244, and/orcognition association module 346 can then match the dorsolateralprefrontal cortex, the posterior parietal cortex, the occipital cortex,and the left premotor area activation measurement to one or morecorresponding mental states. For example, activation of the dorsolateralprefrontal cortex, the posterior parietal cortex, the occipital cortex,and the left premotor area is associated in the literature with brandpreference. Thus an association can be made by association unit 240between preference for the presented image of a product brand andactivation of the dorsolateral prefrontal cortex, the posterior parietalcortex, the occipital cortex, and the left premotor area, for example,based on research findings. This is particularly true when the targetbrand is the subject's favorite brand. See Kenning et al.,“Neuroeconomics: an overview from an economic perspective,” Brain Res.Bull., vol. 67, pp. 343-354 (2005). Further, there is evidence for alarge-scale neural system for visuospatial attention that includes theright posterior parietal cortex. See Cabeza et al, “Imaging CognitionII: An Empirical Review of 275 PET and fMRI Studies,” J. CognitiveNeurosci., vol. 12, pp. 1-47 (2000).

Operation 1604 depicts associating activity in at least one of theinferior precuneus, posterior cingulate, right parietal cortex, rightsuperior frontal gyrus, right supramarginal gyrus, or the ventromedialprefrontal cortex with a mental state indicating brand preference. Forexample, the device 106, association unit 140, emotion associationmodule 242, attention association module 244, and/or cognitionassociation module 346 can associate activity in at least one of theinferior precuneus, posterior cingulate, right parietal cortex, rightsuperior frontal gyrus, right supramarginal gyrus, or the ventromedialprefrontal cortex with a mental state indicating brand preference. Inone embodiment, association unit 240 can receive a brain activitymeasurement indicating activation of the inferior precuneus and theventromedial prefrontal cortex in response to presentation to a memberof population cohort 102 of an avatar associated with a brand as the atleast on characteristic. The brain activity measurement indicatingactivation of the inferior precuneus and the ventromedial prefrontalcortex may be received from, for example, EEG module 320 and/or fMRImodule 316. Emotion association module 242, attention association module244, and/or cognition association module 346 can then match the inferiorprecuneus and ventromedial prefrontal cortex activation measurement toone or more corresponding mental states. For example, activation of theinferior precuneus and the ventromedial prefrontal cortex is associatedin the literature with brand preference. Thus an association can be madebetween preference for the presented avatar associated with a brand andactivation of the inferior precuneus and the ventromedial prefrontalcortex, by association unit 240, for example, based on researchfindings. See Kenning et al., “Neuroeconomics: an overview from aneconomic perspective,” Brain Res. Bull., vol. 67, pp. 343-354 (2005).

Operation 1606 depicts associating activity in at least one of theinsula, the lateral orbital frontal cortex, or the amygdala with amental state indicating emotional disapproval. For example, the device106, association unit 140, and/or emotion association module 242 canassociate activity in at least one of the insula, the lateral orbitalfrontal cortex, or the amygdala with a mental state indicating emotionaldisapproval. In one embodiment, association unit 240 and/or emotionassociation module 342 can receive a brain activity measurementindicating activation of the insula in response to presentation of acharacteristic to a member of population cohort 102. The brain activitymeasurement indicating activation of the insula may be received from,for example, fNIR module 314 and/or EEG module 320. Emotion associationmodule 242 and/or association unit 340 can then match the insulaactivation measurement to one or more corresponding mental states. Forexample, activation of the insula is associated in the literature withpain, distress, and other negative emotional states. Thus an associationcan be made between emotional disapproval of the presentedcharacteristic and activation of the insula, by emotional associationmodule 242 and/or association unit 240, for example, based on researchfindings. See Kenning et al., “Neuroeconomics: an overview from aneconomic perspective,” Brain Res. Bull., vol. 67, pp. 343-354 (2005).

FIG. 17 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 17 illustrates example embodiments where theassociating operation 530 may include at least one additional operation.Additional operations may include operation 1700, 1702, 1704, 1706,and/or 1708.

Operation 1700 depicts associating activity in the dorsolateralprefrontal cortex with a mental state indicating objective approval. Forexample, the device 106, association unit 140, emotion associationmodule 242, attention association module 244, and/or cognitionassociation module 346 can associate activity in the dorsolateralprefrontal cortex with a mental state indicating objective approval. Inone embodiment, association unit 240 can receive a brain activitymeasurement indicating activation of the dorsolateral prefrontal cortexproximate to presentation of a characteristic to a member of populationcohort 102. The brain activity measurement indicating activation of thedorsolateral prefrontal cortex may be received from, for example, MEGmodule 218. Emotion association module 242 and/or association unit 340can then match the dorsolateral prefrontal cortex activation measurementto one or more corresponding mental states. For example, activation ofthe dorsolateral prefrontal cortex is associated in the literature withobjective recognition of benefit despite an emotional perception ofunfairness. Thus an association can be made between activation of theDLPFC and objective approval a presented characteristic, for example byemotion association module 342, for example, based on research findings.See Kenning et al., “Neuroeconomics: an overview from an economicperspective,” Brain Res. Bull., vol. 67, pp. 343-354 (2005).

Operation 1702 depicts associating activity in the caudate nucleus witha mental state indicating trust. For example, the device 106,association unit 140, emotion association module 242, attentionassociation module 244, and/or cognition association module 346 canassociate activity in the caudate nucleus with a mental state indicatingtrust. In one embodiment, association unit 240 can receive a brainactivity measurement indicating activation of the caudate nucleusproximate to presentation of a characteristic to a member of populationcohort 102. The brain activity measurement indicating activation of thecaudate nucleus may be received from, for example, MEG module 218 and/orFNIR module 214. Emotion association module 242 and/or association unit340 can then match the caudate nucleus activation measurement to one ormore corresponding mental states. For example, activation of the caudatenucleus is associated in the literature with trust-building andreciprocity in economic exchange. Thus an association can be madebetween activation of the caudate nucleus and a mental state indicatingtrust in the context of presentation of a characteristic, for example byemotion association module 342, for example, based on research findings.See Kenning et al., “Neuroeconomics: an overview from an economicperspective,” Brain Res. Bull., vol. 67, pp. 343-354 (2005).

Operation 1704 depicts associating activity in the hippocampus with amental state indicating novelty in a perceived object. For example, thedevice 106, association unit 140, emotion association module 242,attention association module 244, and/or cognition association module346 can associate activity in the hippocampus with a mental stateindicating novelty in a perceived object. In one embodiment, associationunit 240 can receive a brain activity measurement indicating activationof the hippocampus proximate to presentation of a characteristic to amember of population cohort 102. The brain activity measurementindicating activation of the hippocampus may be received from, forexample, MEG module 218 and/or fMRI module 316. Emotion associationmodule 242, cognition association module 346, and/or association unit340 can then match the hippocampus activation measurement to one or morecorresponding mental states. For example, activation of the hippocampusis associated in the literature with a central role in processing novelstimuli. Thus an association can be made between activation of thehippocampus and a mental state indicating perceived novelty in thecontext of presentation of a characteristic, for example by cognitionassociation module 346, for example, based on research findings. SeeMartin et al., “Human experience seeking correlates with hippocampusvolume: Convergent evidence from manual tracing and voxel-basedmorphometry,” Neuropsychologia, vol. 45, pp. 2874-81 (2007).

Operation 1706 depicts associating activity in the hippocampus with amental state indicating lack of inhibition toward a perceived object.For example, the device 106, association unit 140, emotion associationmodule 242, attention association module 244, and/or cognitionassociation module 346 can associate activity in the hippocampus with amental state indicating lack of inhibition toward a perceived object. Inone embodiment, association unit 240 can receive a brain activitymeasurement indicating activation of the hippocampus proximate topresentation of a characteristic to a member of population cohort 102.The brain activity measurement indicating activation of the hippocampusmay be received from, for example, PET module 222 and/or FNIR module214. Emotion association module 242, cognition association module 346,and/or association unit 340 can then match the hippocampus activationmeasurement to one or more corresponding mental states. For example,activation of the hippocampus is associated in the literature with alack of inhibition toward a perceived object. Thus an association can bemade between activation of the hippocampus and a mental state indicatinga lack of inhibition toward a perceived object in the context ofpresentation of a characteristic, for example by emotion associationmodule 342, for example, based on research findings. See Wang et al.,“Gastric stimulation in obese subjects activates the hippocampus andother regions involved in brain reward circuitry,” PNAS, vol. 103, pp.15641-45 (2006).

Operation 1708 depicts associating at least one brain activity surrogatemarker with at least one mental state. For example, the device 106,association unit 140, emotion association module 242, attentionassociation module 244, and/or cognition association module 346 canassociate at least one brain activity surrogate marker with at least onemental state. In one embodiment, association unit 240 can receive abrain activity surrogate marker such as a skin response measurement,voice stress measurement, eye movement measurement, and/or iris responsemeasurement proximate to presentation of a characteristic to a member ofpopulation cohort 102. The brain activity surrogate marker may bereceived from, for example, iris response module 232, gaze trackingmodule 234, skin response module 236, and/or voice response module 238.Emotion association module 242, attention association module 244,cognition association module 246, and/or association unit 240 can thenmatch the brain activity surrogate marker to one or more correspondingmental states. For example, detection of voice patterns indicative of acalm mental state may indicate approval toward a characteristic,particularly in combination with brain activation measurement of caudatenucleus activation as a predictor of trust toward the characteristic.Thus an association can be made between a calm voice pattern and amental state indicating approval toward a presented characteristic, forexample by emotion association module 342, for example, based onresearch findings. See Sanfey, “Social Decision-Making: Insights fromGame Theory and Neuroscience,” Science, vol. 318, pp. 598-601 (26 Oct.2007).

FIG. 18 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 18 illustrates example embodiments where thespecifying operation 540 may include at least one additional operation.Additional operations may include operation 1800, 1802, and/or 1804.

Operation 1800 depicts specifying the at least one presentedcharacteristic as the at least one avatar attribute based on the atleast one mental state. For example, device 306, attribute specificationunit 350, voice specification unit 352, and/or non-verbal attributespecification unit 356 can specify the at least one presentedcharacteristic as the at least one avatar attribute based on the atleast one mental state. In one embodiment, attribute specification unit350 can specify a characteristic for presentation to a member ofpopulation cohort 102 such as a photographic image of a girl's face witha specific hairstyle. After measurement of a physiologic activityproximate to the presentation, and association of the physiologicactivity with a mental state, an attribute specification unit 250,non-verbal attribute specification unit 256, and/or body attributespecification unit 262 can specify the specific hairstyle as the atleast one avatar attribute for incorporation into an avatar design. Forexample, if viewing the hairstyle elicited approval in the member ofpopulation cohort 102, specification of an approximation of thehairstyle in the design of an avatar may result in an avatar that isattractive to the member of population cohort 102 and/or populationcohort 104 in general.

Operation 1802 depicts specifying the at least one avatar attributebased on a mental state based on a physiologic activity at or above adefined threshold. For example, device 306, attribute specification unit350, voice specification unit 352, and/or non-verbal attributespecification unit 356 can specify at least one avatar attribute basedon a mental state based on a physiologic activity at or above a definedthreshold. In one embodiment, attribute specification unit 350 canspecify an avatar attribute that is based on a presented characteristicthat elicits, for example, activity in the frontopolar cortex above aspecified intensity level, as measured by, for example, fMRI or FNIR.Because low level activations of the frontopolar cortex may occurfrequently having little or no significance in terms of indicatingmental state (an association unit 240 may nevertheless make anassociation based on low level activation of a brain area or surrogatemarker), a filter may be employed to allow only mental stateassociations founded on activations above a certain threshold to pass toan attribute specification unit 250. Such a filter may be employed atthe level of the physiologic measurement unit 210 such that measurementsthat do not meet or exceed the defined threshold level or intensity arenot transmitted to the association unit 240. Alternatively, a filter maybe employed at the stage of the association unit 240 such that onlyassociations between mental state and physiologic measurements at orabove the defined threshold are transmitted to attribute specificationunit 250. In addition or alternatively, a filter may be employed at thelevel of the attribute specification unit 250 such that mental statesgrounded on physiological measurements below the defined threshold arenot specified.

Operation 1804 depicts specifying the at least one avatar attributebased on a mental state based on a physiologic activity signature. Forexample, device 306, attribute specification unit 350, voicespecification unit 352, and/or non-verbal attribute specification unit356 can specify at least one avatar attribute based on a mental statebased on a physiologic activity signature. In one embodiment, attributespecification unit 350 can specify an avatar attribute that is based ona presented characteristic that elicits, for example, a specificconstellation of brain activity and/or surrogate marker(s) of brainactivity, as measured by, for example, fMRI module 216, FNIR module 214,and/or by surrogate marker measurement unit 230. For example,presentation of a face characteristic to a member of population cohort102 may coincide with detection of activation of the visual cortex(perhaps indicating visual contact with the characteristic); activationof the right prefrontal and parietal areas (perhaps indicating visualvigilance, i.e., attention); activation of the right hemisphere of thebilateral fusiform gyrus (perhaps indicating perception of the facecharacteristic); activation of the medial temporal regions (perhapsindicating retrieval of an item-specific memory); and activation of theventral striatum and decreased activity in the insula (perhapsindicating a positive emotional response such as approval); increasedfrequency of eye movements to the face characteristic; and irisdilation. An association unit 240 may associate some or all of the abovephysiological measurements with one or more mental states, in effectcreating a signature mental state for the reaction of member ofpopulation cohort 102 to the face characteristic; alternatively,association unit 240 may identify a known physiologic activity signatureor pattern that corresponds to a known mental state, i.e., a knownassociation.

FIG. 19 illustrates alternative embodiments of the example operationalflow 500 of FIG. 5. FIG. 19 illustrates example embodiments where theflow 500 may include additional operation 1950.

Operation 1950 depicts performing successive iterations of thepresenting at least one characteristic to at least one member of apopulation cohort, the measuring at least one physiologic activity ofthe at least one member of the population cohort, the at least onephysiologic activity proximate to the at least one presentedcharacteristic, and the associating the at least one physiologicalactivity with at least one mental state. For example, device 106,presentation device 364, physiologic activity measurement unit 310,association unit 340, and/or attribute specification unit 350 mayperform successive iterations of the presenting at least onecharacteristic to at least one member of a population cohort, themeasuring at least one physiologic activity of the at least one memberof the population cohort, the at least one physiologic activityproximate to the at least one characteristic, and the associating the atleast one physiological activity with at least one mental state.

In one embodiment, device 206 can present a characteristic to member ofpopulation cohort 102, during which time physiologic activitymeasurement unit 210, brain activity measurement unit 212, and/orsurrogate marker measurement unit 230 can measure a physiologic activitythat is proximate to the presentation of the characteristic to themember of population cohort 102. Association unit 240, emotionassociation module 242, attention association module 244, and/orcognition association module 246 may then associate the physiologicmeasurement with at least one mental state. Attribute specification unit250 may then specify an avatar attribute based on the mental state, forexample an approving mental state. In a successive iteration, theattribute specification unit 250 may send an avatar including thecharacteristic previously associated with an approving mental state topresentation unit 270 for presentation, perhaps to a different member ofpopulation cohort 104. Alternatively, an avatar may be sent including avariant of the characteristic previously associated with an approvingmental state to presentation unit 270 for presentation to the samemember of population cohort 102 or to a different member of populationcohort 104. Alternatively, an avatar may be sent including a differentcharacteristic to presentation unit 270 for presentation to the samemember of population cohort 102 or to a different member of populationcohort 104. This process may be repeated in rapid succession to gaugepreferences of a member of population cohort 102 and/or other members ofpopulation cohort 104 for attributes of an avatar and/or an avatar aswhole.

FIG. 20 illustrates a partial view of an example computer programproduct 2000 that includes a computer program 2004 for executing acomputer process on a computing device. An embodiment of the examplecomputer program product 2000 is provided using a signal bearing medium2002, and may include one or more instructions for presenting at leastone characteristic to at least one member of a population cohort; one ormore instructions for measuring at least one physiologic activity of theat least one member of the population cohort, the at least onephysiologic activity proximate to the at least one presentedcharacteristic; one or more instructions for associating the at leastone physiological activity with at least one mental state; and one ormore instructions for specifying at least one avatar attribute based onthe at least one mental state. The one or more instructions may be, forexample, computer executable and/or logic-implemented instructions. Inone implementation, the signal-bearing medium 2002 may include acomputer-readable medium 2006. In one implementation, the signal bearingmedium 2002 may include a recordable medium 2008. In one implementation,the signal bearing medium 2002 may include a communications medium 2010.

FIG. 21 illustrates an example system 2100 in which embodiments may beimplemented. The system 2100 includes a computing system environment.The system 2100 also illustrates a member of population cohort 102 usinga device 2104, which is optionally shown as being in communication witha computing device 2102 by way of an optional coupling 2106. Theoptional coupling 2106 may represent a local, wide-area, or peer-to-peernetwork, or may represent a bus that is internal to a computing device(e.g., in example embodiments in which the computing device 2102 iscontained in whole or in part within the device 2104). A storage medium2108 may be any computer storage media. In one embodiment, the computingdevice 2102 may include a virtual machine operating within anothercomputing device. In an alternative embodiment, the computing device2102 may include a virtual machine operating within a program running ona remote server.

The computing device 2102 includes computer-executable instructions 2110that when executed on the computing device 2102 cause the computingdevice 2102 to (a) present at least one characteristic to at least onemember of a population cohort; (b) measure at least one physiologicactivity of the at least one member of the population cohort, the atleast one physiologic activity proximate to the at least one presentedcharacteristic; (c) associate the at least one physiological activitywith at least one mental state; and (d) specify at least one avatarattribute based on the at least one mental state. As referenced aboveand as shown in FIG. 21, in some examples, the computing device 2102 mayoptionally be contained in whole or in part within the device 2104.

In FIG. 21, then, the system 2100 includes at least one computing device(e.g., 2102 and/or 2104). The computer-executable instructions 2110 maybe executed on one or more of the at least one computing device. Forexample, the computing device 2102 may implement the computer-executableinstructions 2110 and output a result to (and/or receive data from) thecomputing device 2104. Since the computing device 2102 may be wholly orpartially contained within the computing device 2104, the device 2104also may be said to execute some or all of the computer-executableinstructions 2110, in order to be caused to perform or implement, forexample, various ones of the techniques described herein, or othertechniques.

The device 2104 may include, for example, a portable computing device,workstation, or desktop computing device. In another example embodiment,the computing device 2102 is operable to communicate with the device2104 associated with the member of population cohort 102 to receiveinformation about physiologic activity of the member of populationcohort 102 for performing data access and data processing, and tospecify at least one avatar attribute based on at least one mentalstate, the mental state based on the physiologic activity of the memberof population cohort 102.

Although a member of population cohort 102 is shown/described herein asa single illustrated figure, those skilled in the art will appreciatethat a member of population cohort 102 and/or member of population 105may be composed of two or more entities. Those skilled in the art willappreciate that, in general, the same may be said of “sender” and/orother entity-oriented terms as such terms are used herein.

Indicating Behavior in a Population Cohort

FIG. 22 illustrates a system 2200 in which embodiments may beimplemented. The system 2200 includes a device 2206. The device 2206 maycontain, for example, a population cohort identification module 2250,which may in turn include a demographic identification module 2252and/or an selection module 2254. The device 2206 may also include device106 (or variants of device 106 such as device 206 or device 306). Thedevice 2206 may interact with at least one member of population cohort102 and/or member of population 105. As discussed above, device 106 canpresent a characteristic to member of population 105 and/or member ofpopulation cohort 102. Further, device 106 can measure at least onephysiologic activity of member of population 105 and/or member ofpopulation cohort 102, as discussed above. Association of the measuredphysiologic activity with a mental state by association unit 140 canresult in attribute specification unit 150 specifying a cohort-linkedavatar and/or cohort-linked avatar attribute. Additional functions ofdevice 106 and its variants device 206 and device 306 are discussedabove in detail, for example in the context of FIG. 2 and FIG. 3. FIG.22 also illustrates the operational flow of FIG. 24, discussed below.Device 2206 may also include a cohort-linked avatar presentation module2260 and/or indication module 2270.

FIG. 23 illustrates an alternate of system 2200 in which embodiments maybe implemented. As described above in the context of FIG. 1 and system100, device 106 may contain, for example, a presentation unit 170, aphysiologic activity measurement unit 110, an association unit 140, anattribute specification unit 150, and/or a population cohortidentification unit 166. Device 106 may interact with one or moremembers of population cohort 104 and/or one or more members ofpopulation 105, including for example, member of population cohort 102.Population cohort 104 may be a part of a population 105. Device 2206 mayreceive a specified cohort-linked avatar attribute and/or cohort-linkedavatar from device 106 and/or attribute specification unit 150. Device2206, population cohort identification module 2250, demographicidentification module, and/or selection module 2254 may identify and/orselect a member of population cohort 102. Cohort-linked avatarpresentation module 2260 and/or indication module 2270 may present tothe identified and/or selected member of population cohort 102 aspecified cohort-linked avatar attribute and/or cohort-linked avatar.

FIG. 24 illustrates an operational flow 2400 representing exampleoperations related to indicating behavior in a population cohort. InFIG. 24 and in following figures that include various examples ofoperational flows, discussion and explanation may be provided withrespect to the above-described system environments of FIGS. 1-4, FIGS.22-23, and/or with respect to other examples and contexts. However, itshould be understood that the operational flows may be executed in anumber of other environment and contexts and/or in modified versions ofFIGS. 1-3 and/or FIGS. 22-23. Also, although the various operationalflows are presented in the sequences illustrated, it should beunderstood that the various operations may be performed in other ordersthan those which are illustrated, or may be performed concurrently.

After a start operation, operation 2410 depicts identifying a member ofa population cohort. For example, a device 2206, population cohortidentification module 2250, demographic identification module 2252,and/or selection module 2254 may identify a member of a populationcohort 102. In one embodiment, population cohort identification module2250 can identify a member of population cohort 102 based on, forexample, video images of the population cohort member 102. For example,demographic module 2252 can analyze, for example, facial features toidentify, for example, a female population cohort member 102 based onface shape and/or hairstyle. In another example, demographic module 2252can analyze video footage of a shopper at a particular store toidentify, for example, an asian shopper based on eye shape and/or skincolor. In one embodiment, a member of population cohort 102 may beidentified by selection module 2254, accessed by a user of system 2200.For example, a user of system 2200 may choose from a menu of availablepopulation cohorts stored in selection module 2254 a particularpopulation cohort as a way of choosing a member of population cohort102. As depicted in FIG. 23, device 2206 and/or population cohortidentification module 2250 may be in communication with device 106 toaccess a specified cohort-linked avatar attribute that corresponds to aselected population cohort. Alternatively, device 2206 may include thecohort-linked avatar attribute specification functionality of system 100and/or device 106.

Operation 2420 depicts indicating at least one behavior in the member ofthe population cohort based on an association between the populationcohort and at least one cohort-linked avatar. For example, a device2206, cohort-linked avatar presentation module 2260, indication module2270, and/or device 106 may indicate at least one behavior in the memberof the population cohort based on an association between the populationcohort and at least one cohort-linked avatar. For example, device 2206,indication module 2270, and/or cohort-linked avatar presentation module2260 can access a specified cohort-linked avatar appropriate for theidentified population cohort, perhaps from device 106. Device 2206,indication module 2270, and/or cohort-linked avatar presentation module2260 can then indicate a future behavior such as an inquiry, an internetsearch, a shopping behavior, and/or a purchasing behavior that is likelyto occur upon presentation of the specified cohort-linked avatar to amember of population cohort 102.

FIG. 25 illustrates alternative embodiments of the example operationalflow 2400 of FIG. 24. FIG. 25 illustrates example embodiments where theidentifying operation 2410 may include at least one additionaloperation. Additional operations may include operation 2500, 2502,and/or operation 2504.

Operation 2500 depicts identifying a member of a demographic populationcohort. For example, a device 2206, population cohort identificationmodule 2250, demographic identification module 2252, and/or selectionmodule 2254 can identify a member of a demographic population cohort. Inone embodiment, a demographic identification module 2252 can identify anage range in a member of population 105 such as “over age 52” as thedemographic population based on an analysis of cloudiness of the eyeassociated with cataracts. In the United States, age-related lenticularchanges have been reported in 42% of those between the ages of 52 to 64,60% of those between the ages 65 and 74, and 91% of those between theages of 75 and 85. Accordingly, may include a camera that can analyze anindividual's eye for cloudiness to identify a member of populationcohort 102.

In another embodiment, demographic module 2252 may identify a populationmember's ethnicity based on detection of a foreign language or accentedspeech, such as a southern accent, a Boston accent, a Spanish accent, aBritish accent, or the like. See, for example, U.S. Pat. No. 7,263,489“Detection of characteristics of human-machine interactions for dialogcustomization and analysis.”

In another embodiment, demographic module 2252 may identify a populationmember as part of a hearing-impaired demographic based on body languagesuch as use of American Sign Language, for example captured by videofootage of the population member.

Operation 2502 depicts identifying a member of at least one of an age,gender, or ethnicity population cohort. For example, a device 2206,population cohort identification module 2250, demographic identificationmodule 2252, and/or selection module 2254 can identify a member of atleast one of an age, gender, or ethnicity population cohort. In oneembodiment, a demographic identification module 2252 can identify amember of an age, gender, or ethnicity population cohort based onvisible an individual's objects such as clothing, hairstyle, and/oradornment such as jewelry, which may be particularly informative as anindicators of gender, age, and/or ethnicity. For example, a dress mayhelp identify a population member's gender as female, and similarly asuit with a necktie may help identify a population member's gender asmale.

Further, images of the population member's face may be analyzed bydemographic module 2252 to identify a gender by face shape. See Jain etal., “Gender identification using frontal facial images,” IEEEMultimedia and Expo International Conference, 4 pp. (6-8 Jul. 2005);describing gender classification using frontal facial images in which96% accuracy was reached using a Support Vector Machine (SVM) inindependent component analysis (ICA) space. Of course other methods ofgender identification known in the art may be used. Voice analysis mayalso be used to identify a particular gender.

A number of methods of identifying ethnicity based on facial featuresare known in the art, for example, ethnicity identification may beformulated as a two-category classification problem, for example, toclassify the subject as an Asian or non-Asian. The input images may beresized to different scales. At each scale, a classic appearance-basedface recognizer based on a linear discriminant analysis representationmay be developed under a Bayesian statistical decision framework. Anensemble may then be constructed by integrating classification resultsto arrive at a final decision. The product rule may be used as anintegration strategy. See Lu et al., “Ethnicity Identification from FaceImages,” Biometric Technology for Human Identification, Eds. Jain etal., Proc. SPIE, Vol. 5404, pp. 114-123 (2004).

User ethnicity identification may be based on a number of factorsincluding skin and/or hair characteristics associated with ethnicity,such as red hair among Caucasians; voice and/or speech associated withethnicity, such as French-accented English indicating French orFrench-Canadian ethnicity; face pattern associated with ethnicity, suchas eye shape, nose shape, face shape, or the like; and eye attributessuch as blue eyes among Caucasians. In one embodiment, Gabor waveletstransformation and retina sampling from user-health test functionoutputs may be combined to extract key facial features, and supportvector machines may be used for ethnicity classification. Anexperimental system has used Gabor wavelets transformation and retinasampling in combination to extract key facial features, and supportvector machines were used for ethnicity classification, resulting inapproximately 94% success for ethnicity estimation under variouslighting conditions. See Hosoi et al., “Ethnicity estimation with facialimages,” Sixth IEEE International Conference on Automatic Face andGesture Recognition, pp. 195-200 (2004). Of course other methods ofethnicity identification known in the art may be used.

In another embodiment, an age, gender, and/or ethnicity characteristicmay be based on, for example, an iris pattern associated with an asianuser. For example, a bank of multichannel 2D Gabor filters may be usedby demographic module 2252 to capture global texture information aboutan a user's iris, and AdaBoost, a machine learning algorithm, may beused to allow a demographic module 2252 to learn a discriminantclassification principle from a pool of candidate iris feature sets.Iris image data may be thus grouped into race categories, for example,Asian and non-Asian. See Qui et al., “Global Texture Analysis of IrisImages for Ethnic Classification,” Lecture notes in computer science,Springer:Berlin/Heidelberg, Advances in Biometrics, pp. 411-418 (2005).

The age, gender, and/or ethnicity characteristic also may be based on,for example, a face pattern analysis indicating facial hair (e.g., abeard or moustache) signifying to the demographic module 2252 that theuser is male. The age, gender, and/or ethnicity characteristic also maybe based on, for example, speech or voice data such as MandarinChinese-accented Chinese or English speech signifying to the demographicmodule 2252 that the population member is of Chinese ethnicity. Forexample, three demographic characteristics may be transmitted in theform of “elderly Chinese male.”

Alternatively, iris pattern data, face pattern data, and voice or speechdata may each independently relate to one demographic characteristic,such as gender. For example, iris pattern data may indicate a male user,face pattern data detecting facial hair may indicate a male user, andvoice pitch data may indicate a male user, resulting in a male genderdemographic population cohort identification with a relatively highlevel of confidence.

Operation 2504 depicts selecting a member of a population cohort. Forexample, a device 2206, population cohort identification module 2250,demographic identification module 2252, and/or selection module 2254 canselect a member of a population cohort. For example, selection module2254 may select group of local shoppers as a population cohort. In oneembodiment, selection module 2254 may access from device 106, forexample, a product avatar such as a BMW automobile that is associatedwith preference among, for example, Microsoft employees working inRedmond, Wash. Thus the BMW automobile may be a specified cohort-linkedavatar that is known to be associated with, for example, a mental stateindicating preference among a group of Microsoft employees working inRedmond, Wash. Accordingly, selection module 2254 may select theMicrosoft employees working in Redmond, Wash. as a population cohort.Alternatively, a user of system 2200 may select an establisheddemographic group as the population cohort and/or an ad hoc populationcohort.

FIG. 26 illustrates alternative embodiments of the example operationalflow 2400 of FIG. 24. FIG. 26 illustrates example embodiments where theindicating operation 2420 may include at least one additional operation.Additional operations may include operation 2600, 2602, and/or operation2604.

Operation 2600 depicts indicating a likely behavior of at least onemember of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar. Forexample, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can indicate a likely behavior of at leastone member of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar. Forexample, indication module 2270 can coordinate with a cohort-linkedavatar presentation module 2260 to evaluate a context of presentation ofa cohort-linked avatar to a member of population cohort 102, forexample, during a shopping experience (e.g., on a monitor at a shoppingmall, within a webpage, or on a personal electronic device of the memberof population cohort 102). In some embodiments, given a context thatprovides access to marketed items, indication module 2270 may provide anindication of a likelihood of a shopping and/or purchase behaviorcontingent upon presenting the image of a cohort-linked avatar and/orcohort-linked avatar attribute to the member of population cohort 102,particularly where the mental state associated with the avatar orattribute is a positive one such as preference.

For example, indication module 2270 may provide a likelihood of aninquiry behavior as an indication of a behavior upon presentation of acohort-linked avatar such as a particular furniture design to a memberof a population cohort 102, the cohort-linked avatar furniture designassociated with approval in members of the population cohort, perhapsconsisting of homeowners living in a specific zip code. Other behaviorsassociated with such feelings of approval in the context of thecohort-linked avatar furniture design may also be indicated, such asincreased eye movements toward the cohort-linked avatar furnituredesign, increased attention toward the cohort-linked avatar furnituredesign, and/or active steps to investigate and/or purchase thecohort-linked avatar furniture design and/or items associated with thecohort-linked avatar furniture design.

Operation 2602 depicts forecasting at least one behavior of at least onemember of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar. Forexample, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can forecast at least one behavior of atleast one member of the population cohort based on an associationbetween the population cohort and the at least one cohort-linked avatar.For example, indication module 2270 can forecast a click-throughbehavior in a member of population cohort 102 upon presentation of acohort-linked avatar in an advertisement to the member of populationcohort 102, for example, during an interaction with a social networkingsite. In some embodiments, given a context that provides access to itemsthat are easily selected for further action, such as items on webpages,indication module 2270 may provide a forecast of a clicking, typing,searching, shopping and/or purchasing behavior contingent uponpresenting, for example, an the image of a cohort-linked avatar and/orcohort-linked avatar attribute to the member of population cohort 102,particularly where the mental state associated with the avatar orattribute is a positive one such as product preference.

Operation 2604 depicts predicting at least one behavior of at least onemember of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar. Forexample, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can predict at least one behavior of atleast one member of the population cohort based on an associationbetween the population cohort and the at least one cohort-linked avatar.For example, indication module 2270 can predict a purchasing behavior asa result of cohort-linked avatar presentation module 2260 presenting animage of a cohort-linked avatar to a member of population cohort 102,for example, during a shopping experience (e.g., in a shop window, on afacebook page, and/or on a personal electronic device of the member ofpopulation cohort 102). For example, a cohort-linked avatar musicattribute may be specified which is associated with approval in apopulation demographic that is known to buy music at a particularinternet outlet. Indication module 2270 may predict that selectivepresentation of the cohort-linked avatar music attribute to members ofpopulation cohort 104 will result in members of the population cohort104 listening to the cohort-linked music attribute and/or other musicassociated with the cohort-linked music attribute during a given visitto the particular internet outlet.

FIG. 27 illustrates alternative embodiments of the example operationalflow 2400 of FIG. 24. FIG. 27 illustrates example embodiments where theindicating operation 2420 may include at least one additional operation.Additional operations may include operation 2700, 2702, 2704, and/oroperation 2706.

Operation 2700 depicts indicating at least one purchasing behavior inthe member of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar. Forexample, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can indicate at least one purchasingbehavior in the member of the population cohort based on an associationbetween the population cohort and the at least one cohort-linked avatar.In one embodiment, a cohort-linked avatar presentation module 2260 canpresent to, for example, a group of registered Xbox LIVE users acohort-linked avatar in the form of an image of Master Chief from theHalo franchise of computer games, which has been associated with anapproving mental state among one or more registered Xbox LIVE users. Inone embodiment, the image of Master Chief may be paired with one or moremarketed items, such that the approving mental state associated with thecohort-linked avatar Master Chief image may be transferred to the paireditem. In this way, indication module 2270 can indicate a likelihood thata member of population cohort 102 may purchase the paired item by virtueof its association with the cohort-linked avatar.

Operation 2702 depicts indicating at least one internet usage behaviorin the member of the population cohort based on an association betweenthe population cohort and the at least one cohort-linked avatar. Forexample, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can indicate at least one internet usagebehavior in the member of the population cohort based on an associationbetween the population cohort and the at least one cohort-linked avatar.In one embodiment, a cohort-linked avatar presentation module 2260 canpresent to, for example, a member of population cohort 102 acohort-linked avatar in the form of an audio clip that has beenassociated with a feeling of reward among one or more members ofpopulation cohort 104. In one embodiment, an audio clip may be presented(e.g., played) during the presentation of a photo and/or video image ofa marketed item, such that the feeling of reward associated with thecohort-linked avatar audio clip may be transferred to the paired itemimage. In this way, indication module 2270 can indicate that a member ofpopulation cohort 102 may research, shop for, and/or purchase the paireditem by virtue of its association with the cohort-linked avatar. Forexample, cohort-linked avatar presentation module 2260 can play an audioclip such as a Bart Simpson tag line such as “Eat my shorts!” during thepresentation of an image on a website such as a hyperlinked image of amarketed toy to a member of population cohort 102. Accordingly,indication module 2270 can predict that the member of population cohort102 will associate a feeling of reward with the audio clip and the imageof a toy, and will click on the hyperlinked image of the toy to learnmore about it and/or to purchase it.

Operation 2704 depicts indicating at least one shopping behavior in themember of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar. Forexample, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can indicate at least one shoppingbehavior in the member of the population cohort based on an associationbetween the population cohort and the at least one cohort-linked avatar.In one embodiment, a cohort-linked avatar presentation module 2260 canpresent to, for example, a member of population cohort 102 acohort-linked avatar in the form of a computer-generated figure thatincorporates several cohort-linked attributes that have been associatedwith a feeling of attention, trust, approval, and/or preference amongone or more members of population cohort 104. In one embodiment, avirtual personal shopper in the form of a teenage girl on a website maybe presented in which various cohort-linked attributes of Hollywoodcelebrities such as Paris Hilton, Britney Spears, and Angelina Jolie arecombined in the form of the teenage girl virtual personal shopper as acohort-linked avatar that appeals to teenage girls aged 14-19. In thisexample, the virtual personal shopper bearing, for example, a likenessof Paris Hilton's face and hair, a t-shirt with a line from a BritneySpears song like “Oops I did it again,” and a voice like that ofAngelina Jolie may be presented to a member of population cohort 102such that a feeling of attention, trust, approval, and/or preference isevoked. In this way, online shopping activities conducted with thecohort-linked avatar virtual personal shopper may benefit from thepositive views toward the cohort-linked avatar virtual personal shopperamong members of population cohort 104 and member of population cohort102 specifically. Accordingly, indication module 2270 may indicatefuture behavior of population cohort member 102 such as browsing awebsite, looking at a product, placing an item on a wish list,communicating with a friend about a product, and/or purchasing an itemonline by virtue of the association with the cohort-linked avatarpersonal virtual shopper.

FIG. 28 illustrates alternative embodiments of the example operationalflow 2400 of FIG. 24. FIG. 28 illustrates example embodiments where theindicating operation 2420 may include at least one additional operation.Additional operations may include operation 2800, 2802, and/or operation2804.

Operation 2800 depicts indicating at least one gameplay behavior in themember of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar. Forexample, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can indicate at least one gameplaybehavior in the member of the population cohort based on an associationbetween the population cohort and the at least one cohort-linked avatar.In one embodiment, in an online gaming universe, a specific robot designmay be a cohort-linked avatar that is associated with approval and/ortrust in a population cohort 104 consisting of members of a factionwithin the gaming universe. Accordingly, indication module 2270 mayforecast a concordant action and/or a friendly communication from amember of the faction subsequent to presentation of the cohort-linkedavatar robot design to the member of the faction, because of feelings oftrust and/or approval associated with the presented cohort-linked avatarrobot design.

Operation 2802 depicts indicating at least one social interactionbehavior in the member of the population cohort based on an associationbetween the population cohort and the at least one cohort-linked avatar.For example, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can indicate at least one socialinteraction behavior in the member of the population cohort based on anassociation between the population cohort and the at least onecohort-linked avatar. In one embodiment, a social networking websitesuch as facebook and/or myspace may attach its brand to a cohort-linkedavatar sports figure that is associated with approval and/or preferencein a population cohort 104 consisting of young girls who are fans of atennis star, such as Selena Williams. Accordingly, indication module2270 may predict that other members of the population cohort 104 will,for example, register with facebook and/or myspace, create a facebookand/or myspace page, and/or sign up as a friend of Selena Williams onSelena Williams' facebook and/or myspace page subsequent to presentationof a cohort-linked avatar image of Selena Williams and/or acohort-linked avatar attribute of Selena Williams together with afacebook and/or myspace brand identification, because of feelings ofapproval and/or preference associated with, for example, the presentedcohort-linked avatar image of Selena Williams.

Operation 2804 depicts indicating at least one behavior in at least oneof an online shopper, an online gamer, a virtual world participant, or asocial networking site participant based on an association between thepopulation cohort and the at least one cohort-linked avatar. Forexample, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can indicate at least one behavior in atleast one of an online shopper, an online gamer, a virtual worldparticipant, or a social networking site participant based on anassociation between the population cohort and the at least onecohort-linked avatar. In one embodiment, a cohort-linked avatar musicalgroup that is associated with approval and/or preference in a populationcohort 104 consisting of Austin, Tex. residents aged 20-40 may bepresented to a member of the population cohort 104 at an event in avirtual world that involves marketing of a local Austin, Tex. business.Accordingly, indication module 2270 may indicate a likelihood thatmembers of the population cohort 104 in the virtual world will, forexample, take steps to learn more about the local Austin, Tex. businesssubsequent to presentation of the cohort-linked avatar musical grouptogether with the local Austin, Tex. business, because of feelings ofapproval and/or preference associated with the presented cohort-linkedavatar musical group.

FIG. 29 illustrates alternative embodiments of the example operationalflow 2400 of FIG. 24. FIG. 29 illustrates example embodiments where theindicating operation 2420 may include at least one additional operation.Additional operations may include operation 2900, 2902, and/or operation2904.

Operation 2900 depicts indicating at least one behavior in a member of ademographic population based on an association between the demographicpopulation and at least one avatar linked to the demographic population.For example, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can indicate at least one behavior in amember of a demographic population based on an association between thedemographic population and at least one avatar linked to the demographicpopulation. In one embodiment, a cohort-linked avatar audio clip ofGeorge Clooney's voice associated with approval and/or preference in apopulation cohort 104 consisting of women aged 20-60 as a demographicpopulation may be presented to a member of the population cohort 104 aspart of an advertisement for a men's cologne, for example. Accordingly,indication module 2270 may predict that a woman aged 20-60 to whom thecohort-linked avatar audio clip of George Clooney's voice with theadvertisement is presented may, for example, shop for the cologne as aresult of feelings of approval and/or preference associated with thepresented cohort-linked avatar audio clip of George Clooney's voice.

Identification of a demographic population may be based on a fact-basedattribute of a population cohort. For example, a population cohort suchas individuals wearing a dress at the time of product purchase mayresult in classification of that population cohort as belonging to afemale gender demographic. Such a classification, while not perfect, mayprovide a likelihood of gender that may be confirmed by other means,such as detection of face shape by an imaging device, detection of voicepitch and/or speech pattern, name association with gender (e.g., Maryand Jane as female names), or the like. Of course, information about thedemographics of a population cohort member may be provided directly bythe member of population cohort 102, perhaps as part of a permissionsprocess, or perhaps indirectly by a witness to the presentation of acohort-linked avatar attribute to a population cohort member.

Operation 2902 depicts indicating at least one behavior in the member ofthe population cohort based on a statistical association between thepopulation cohort and the at least one cohort-linked avatar. Forexample, device 2206, cohort-linked avatar presentation module 2260,and/or indication module 2270 can indicate at least one behavior in themember of the population cohort based on a statistical associationbetween the population cohort and the at least one cohort-linked avatar.In one embodiment, cohort-linked avatar presentation module 2260 mayselect for presentation to members of population cohort 104 one of a setof cohort-linked avatars, each of which are associated with approval ina population cohort 104 consisting of male NASCAR race attendees aged20-50, for example. Such a set of cohort-linked avatars, for example, agroup of beef jerky brands, may be evaluated by device 2206 for the moststatistically valid one of the group, to be presented by cohort-linkedavatar presentation module 2260. For example, device 2206, cohort-linkedavatar presentation module 2260, and/or indication module 2270 may rankthe cohort-linked avatar beef jerky brands by the number of populationcohort members measured to establish the association between measuredphysiologic activity and approval; by degree of physiologic activityassociated with presentation of the beef jerky brand in the measuredpopulation cohort members; by geographical location of the measurements,or the like. Accordingly, indication module 2270 may identify onecohort-linked avatar beef jerky brand, such as Jeff Foxworthy's beefjerky, as the one that is most likely to be purchased if cohort-linkedavatar presentation module 2260 presents a cohort-linked avatar JeffFoxworthy's beef jerky to males aged 20-50 at a particular NASCAR event,based on higher approval associated with the cohort-linked avatar JeffFoxworthy's beef jerky compared to that of other cohort-linked avatarbeef jerky brands.

Operation 2904 depicts indicating at least one behavior in the member ofthe population cohort based on an association between the populationcohort and at least one avatar linked to the member of the populationcohort. For example, device 2206, cohort-linked avatar presentationmodule 2260, and/or indication module 2270 can indicate at least onebehavior in the member of the population cohort based on an associationbetween the population cohort and at least one avatar linked to themember of the population cohort. In one embodiment, cohort-linked avatarpresentation module 2260 may present to a member of population cohort102 a cohort-linked avatar that was specified based on one or morephysiologic measurements of the member of population cohort 102. Forexample, an individual's behavior may be predicted based on a reactionto a custom-specified cohort-linked avatar that is specific to theindividual as a member of population cohort 102.

For example, device 2206 and/or device 106 may present an instance ofmedia content such as an image of a personal electronic device like aniphone or a Zune media player to an individual as a member of populationcohort 102. Device 2206 and/or device 106 may then measure at least onephysiologic activity at a time proximate to presentation of the Zune,for example. Following association of the measured physiologic activity,such as brain activity measured by FNIR module 314, with a mental statesuch as preference, attribute specification unit 350 may specify anattribute of the Zune and/or the image of the Zune, for example, as acohort-linked avatar associated with preference in the member ofpopulation cohort 102. Accordingly, presentation of the cohort-linkedavatar Zune image to the same member of population cohort 102 in othercontexts may provide a basis for indication module 2270 to predicts abehavior in the member of population cohort 102. For example,cohort-linked avatar presentation module 2260 may present to the memberof population cohort 102 the cohort-linked avatar Zune image togetherwith a particular audio clip of a new band, such that indication module2270 can predict that the member of population cohort 102 will listen tothe audio clip or a sample of the audio clip, and/or purchase a songand/or collection of songs by the new band.

FIG. 30 illustrates a partial view of an example computer programproduct 3000 that includes a computer program 3004 for executing acomputer process on a computing device. An embodiment of the examplecomputer program product 3000 is provided using a signal bearing medium3002, and may include one or more instructions for identifying a memberof a population cohort; and one or more instructions for indicating atleast one behavior in the member of the population cohort based on anassociation between the population cohort and at least one cohort-linkedavatar. The one or more instructions may be, for example, computerexecutable and/or logic-implemented instructions. In one implementation,the signal-bearing medium 3002 may include a computer-readable medium3006. In one implementation, the signal bearing medium 3002 may includea recordable medium 3008. In one implementation, the signal bearingmedium 3002 may include a communications medium 3010.

FIG. 31 illustrates an example system 3100 in which embodiments may beimplemented. The system 3100 includes a computing system environment.The system 3100 also illustrates an entity 3105 using a device 3104,which is optionally shown as being in communication with a computingdevice 3102 by way of an optional coupling 3106. The optional coupling3106 may represent a local, wide-area, or peer-to-peer network, or mayrepresent a bus that is internal to a computing device (e.g., in exampleembodiments in which the computing device 3102 is contained in whole orin part within the device 3104). A storage medium 3108 may be anycomputer storage media. In one embodiment, the computing device 3102 mayinclude a virtual machine operating within another computing device. Inan alternative embodiment, the computing device 3102 may include avirtual machine operating within a program running on a remote server.

The computing device 3102 includes computer-executable instructions 3110that when executed on the computing device 3102 cause the computingdevice 3102 to (a) identify a member of a population cohort; and (b)indicate at least one behavior in the member of the population cohortbased on an association between the population cohort and at least onecohort-linked avatar. As referenced above and as shown in FIG. 31, insome examples, the computing device 3102 may optionally be contained inwhole or in part within the device 3104. Computing device 3102 mayoptionally include any or all of the functions of device 2602 and/ordevice 106.

In FIG. 31, then, the system 3100 includes at least one computing device(e.g., 3102 and/or 3104). The computer-executable instructions 3110 maybe executed on one or more of the at least one computing device. Forexample, the computing device 3102 may implement the computer-executableinstructions 3110 and output a result to (and/or receive data from) thecomputing device 3104. Since the computing device 3102 may be wholly orpartially contained within the computing device 3104, the device 3104also may be said to execute some or all of the computer-executableinstructions 3110, in order to be caused to perform or implement, forexample, various ones of the techniques described herein, or othertechniques.

The device 3104 may include, for example, a portable computing device,workstation, or desktop computing device. In another example embodiment,the computing device 3102 is operable to communicate with the device3104 associated with the entity 3105 to receive information such aspopulation cohort data and/or specified cohort-linked avatar attributedata from a device 3105 for performing data access and data processing.

Although an entity 3105 is shown/described herein as a singleillustrated figure, those skilled in the art will appreciate that anentity 3105 may be composed of two or more entities. Those skilled inthe art will appreciate that, in general, the same may be said of“sender” and/or other entity-oriented terms as such terms are usedherein.

One skilled in the art will recognize that the herein-describedcomponents (e.g., steps), devices, and objects and the discussionaccompanying them are used as examples for the sake of conceptualclarity and that various configuration modifications are within theskill of those in the art. Consequently, as used herein, the specificexemplars set forth and the accompanying discussion are intended to berepresentative of their more general classes. In general, use of anyspecific exemplar herein is also intended to be representative of itsclass, and the non-inclusion of such specific components (e.g., steps),devices, and objects herein should not be taken as indicating thatlimitation is desired.

Those skilled in the art will appreciate that the foregoing specificexemplary processes and/or devices and/or technologies arerepresentative of more general processes and/or devices and/ortechnologies taught elsewhere herein, such as in the claims filedherewith and/or elsewhere in the present application.

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware and software implementations of aspects of systems; theuse of hardware or software is generally (but not always, in that incertain contexts the choice between hardware and software can becomesignificant) a design choice representing cost vs. efficiency tradeoffs.Those having skill in the art will appreciate that there are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; alternatively, if flexibility is paramount, theimplementer may opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware. Hence, there are several possible vehicles bywhich the processes and/or devices and/or other technologies describedherein may be effected, none of which is inherently superior to theother in that any vehicle to be utilized is a choice dependent upon thecontext in which the vehicle will be deployed and the specific concerns(e.g., speed, flexibility, or predictability) of the implementer, any ofwhich may vary. Those skilled in the art will recognize that opticalaspects of implementations will typically employ optically-orientedhardware, software, and or firmware.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link, etc.).

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware, orany combination thereof can be viewed as being composed of various typesof “electrical circuitry.” Consequently, as used herein “electricalcircuitry” includes, but is not limited to, electrical circuitry havingat least one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, electrical circuitry forming ageneral purpose computing device configured by a computer program (e.g.,a general purpose computer configured by a computer program which atleast partially carries out processes and/or devices described herein,or a microprocessor configured by a computer program which at leastpartially carries out processes and/or devices described herein),electrical circuitry forming a memory device (e.g., forms of randomaccess memory), and/or electrical circuitry forming a communicationsdevice (e.g., a modem, communications switch, or optical-electricalequipment). Those having skill in the art will recognize that thesubject matter described herein may be implemented in an analog ordigital fashion or some combination thereof.

Those skilled in the art will recognize that it is common within the artto describe devices and/or processes in the fashion set forth herein,and thereafter use engineering practices to integrate such describeddevices and/or processes into data processing systems. That is, at leasta portion of the devices and/or processes described herein can beintegrated into a data processing system via a reasonable amount ofexperimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

All of the above U.S. patents, U.S. patent application publications,U.S. patent applications, foreign patents, foreign patent applicationsand non-patent publications referred to in this specification and/orlisted in any Application Data Sheet are incorporated herein byreference, to the extent not inconsistent herewith.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled,” to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable,” to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations are not expressly set forth herein for sakeof clarity.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.Furthermore, it is to be understood that the invention is defined by theappended claims. It will be understood by those within the art that, ingeneral, terms used herein, and especially in the appended claims (e.g.,bodies of the appended claims) are generally intended as “open” terms(e.g., the term “including” should be interpreted as “including but notlimited to,” the term “having” should be interpreted as “having atleast,” the term “includes” should be interpreted as “includes but isnot limited to,” etc.). It will be further understood by those withinthe art that if a specific number of an introduced claim recitation isintended, such an intent will be explicitly recited in the claim, and inthe absence of such recitation no such intent is present. For example,as an aid to understanding, the following appended claims may containusage of the introductory phrases “at least one” and “one or more” tointroduce claim recitations. However, the use of such phrases should notbe construed to imply that the introduction of a claim recitation by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

With respect to the appended claims, those skilled in the art willappreciate that recited operations therein may generally be performed inany order. Examples of such alternate orderings may include overlapping,interleaved, interrupted, reordered, incremental, preparatory,supplemental, simultaneous, reverse, or other variant orderings, unlesscontext dictates otherwise. With respect to context, even terms like“responsive to,” “related to,” or other past-tense adjectives aregenerally not intended to exclude such variants, unless context dictatesotherwise.

1. A method comprising: identifying a member of a population cohort; andindicating, using a suitable microprocessor, at least one behavior inthe member of the population cohort based on an association between thepopulation cohort and at least one cohort-linked avatar, where the atleast one cohort-linked avatar is at least partly based on a mentalstate associated with at least one physiologic activity measurement, andwhere at least one of a weight or a priority is assigned to at least oneof a reference mental state or at least one reference physiologicactivity measurement.
 2. The method of claim 1 wherein the identifying amember of a population cohort comprises: identifying a member of ademographic population cohort.
 3. The method of claim 1 wherein theidentifying a member of a population cohort comprises: identifying amember of at least one of an age, gender, or ethnicity populationcohort.
 4. The method of claim 1 wherein the identifying a member of apopulation cohort comprises: selecting a member of a population cohort.5. The method of claim 1 wherein the indicating at least one behavior inthe member of the population cohort based on an association between thepopulation cohort and at least one cohort-linked avatar comprises:indicating a likely behavior of at least one member of the populationcohort based on an association between the population cohort and the atleast one cohort-linked avatar.
 6. The method of claim 1 wherein theindicating at least one behavior in the member of the population cohortbased on an association between the population cohort and at least onecohort-linked avatar comprises: forecasting at least one behavior of atleast one member of the population cohort based on an associationbetween the population cohort and the at least one cohort-linked avatar.7. The method of claim 1 wherein the indicating at least one behavior inthe member of the population cohort based on an association between thepopulation cohort and at least one cohort-linked avatar comprises:predicting at least one behavior of at least one member of thepopulation cohort based on an association between the population cohortand the at least one cohort-linked avatar.
 8. The method of claim 1wherein the indicating at least one behavior in the member of thepopulation cohort based on an association between the population cohortand at least one cohort-linked avatar comprises: indicating at least onepurchasing behavior in the member of the population cohort based on anassociation between the population cohort and the at least onecohort-linked avatar.
 9. The method of claim 1 wherein the indicating atleast one behavior in the member of the population cohort based on anassociation between the population cohort and at least one cohort-linkedavatar comprises: indicating at least one internet usage behavior in themember of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar.
 10. Themethod of claim 1 wherein the indicating at least one behavior in themember of the population cohort based on an association between thepopulation cohort and at least one cohort-linked avatar comprises:indicating at least one shopping behavior in the member of thepopulation cohort based on an association between the population cohortand the at least one cohort-linked avatar.
 11. The method of claim 1wherein the indicating at least one behavior in the member of thepopulation cohort based on an association between the population cohortand at least one cohort-linked avatar comprises: indicating at least onegameplay behavior in the member of the population cohort based on anassociation between the population cohort and the at least onecohort-linked avatar.
 12. The method of claim 1 wherein the indicatingat least one behavior in the member of the population cohort based on anassociation between the population cohort and at least one cohort-linkedavatar comprises: indicating at least one social interaction behavior inthe member of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar.
 13. Themethod of claim 1 wherein the indicating at least one behavior in themember of the population cohort based on an association between thepopulation cohort and at least one cohort-linked avatar comprises:indicating at least one behavior in at least one of an online shopper,an online gamer, a virtual world participant, or a social networkingsite participant based on an association between the population cohortand the at least one cohort-linked avatar.
 14. The method of claim 1wherein the indicating at least one behavior in the member of thepopulation cohort based on an association between the population cohortand at least one cohort-linked avatar comprises: indicating at least onebehavior in a member of a demographic population based on an associationbetween the demographic population and at least one avatar linked to thedemographic population.
 15. The method of claim 1 wherein the indicatingat least one behavior in the member of the population cohort based on anassociation between the population cohort and at least one cohort-linkedavatar comprises: indicating at least one behavior in the member of thepopulation cohort based on a statistical association between thepopulation cohort and the at least one cohort-linked avatar.
 16. Themethod of claim 1 wherein the indicating at least one behavior in themember of the population cohort based on an association between thepopulation cohort and at least one cohort-linked avatar comprises:indicating at least one behavior in the member of the population cohortbased on an association between the population cohort and at least oneavatar linked to the member of the population cohort.
 17. A systemcomprising: circuitry for identifying a member of a population cohort;and circuitry for indicating at least one behavior in the member of thepopulation cohort based on an association between the population cohortand at least one cohort-linked avatar, where the at least onecohort-linked avatar is at least partly based on a mental stateassociated with at least one physiologic activity measurement, and whereat least one of a weight or a priority is assigned to at least one of areference mental state or at least one reference physiologic activitymeasurement.
 18. The system of claim 17 wherein the circuitry foridentifying a member of a population cohort comprises: circuitry foridentifying a member of a demographic population cohort.
 19. The systemof claim 17 wherein the circuitry for identifying a member of apopulation cohort comprises: circuitry for identifying a member of atleast one of an age, gender, or ethnicity population cohort.
 20. Thesystem of claim 17 wherein the circuitry for identifying a member of apopulation cohort comprises: circuitry for selecting a member of apopulation cohort.
 21. The system of claim 1 wherein the circuitry forindicating at least one behavior in the member of the population cohortbased on an association between the population cohort and at least onecohort-linked avatar comprises: circuitry for indicating a likelybehavior of at least one member of the population cohort based on anassociation between the population cohort and the at least onecohort-linked avatar.
 22. The system of claim 1 wherein the circuitryfor indicating at least one behavior in the member of the populationcohort based on an association between the population cohort and atleast one cohort-linked avatar comprises: circuitry for forecasting atleast one behavior of at least one member of the population cohort basedon an association between the population cohort and the at least onecohort-linked avatar.
 23. The method of claim 1 wherein the circuitryfor indicating at least one behavior in the member of the populationcohort based on an association between the population cohort and atleast one cohort-linked avatar comprises: circuitry for predicting atleast one behavior of at least one member of the population cohort basedon an association between the population cohort and the at least onecohort-linked avatar.
 24. The system of claim 17 wherein the circuitryfor indicating at least one behavior in the member of the populationcohort based on an association between the population cohort and atleast one cohort-linked avatar comprises: circuitry for indicating atleast one purchasing behavior in the member of the population cohortbased on an association between the population cohort and the at leastone cohort-linked avatar.
 25. The system of claim 17 wherein thecircuitry for indicating at least one behavior in the member of thepopulation cohort based on an association between the population cohortand at least one cohort-linked avatar comprises: circuitry forindicating at least one internet usage behavior in the member of thepopulation cohort based on an association between the population cohortand the at least one cohort-linked avatar.
 26. The system of claim 17wherein the circuitry for indicating at least one behavior in the memberof the population cohort based on an association between the populationcohort and at least one cohort-linked avatar comprises: circuitry forindicating at least one shopping behavior in the member of thepopulation cohort based on an association between the population cohortand the at least one cohort-linked avatar.
 27. The system of claim 17wherein the circuitry for indicating at least one behavior in the memberof the population cohort based on an association between the populationcohort and at least one cohort-linked avatar comprises: circuitry forindicating at least one gameplay behavior in the member of thepopulation cohort based on an association between the population cohortand the at least one cohort-linked avatar.
 28. The system of claim 17wherein the circuitry for indicating at least one shopping behavior inthe member of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar comprises:circuitry for indicating at least one social interaction behavior in themember of the population cohort based on an association between thepopulation cohort and the at least one cohort-linked avatar.
 29. Thesystem of claim 17 wherein the circuitry for indicating at least oneshopping behavior in the member of the population cohort based on anassociation between the population cohort and the at least onecohort-linked avatar comprises: circuitry for indicating at least onebehavior in at least one of an online shopper, an online gamer, avirtual world participant, or a social networking site participant basedon an association between the population cohort and the at least onecohort-linked avatar.
 30. The system of claim 17 wherein the circuitryfor indicating at least one shopping behavior in the member of thepopulation cohort based on an association between the population cohortand the at least one cohort-linked avatar comprises: circuitry forindicating at least one behavior in a member of a demographic populationbased on an association between the demographic population and at leastone avatar linked to the demographic population.
 31. The system of claim17 wherein the circuitry for indicating at least one shopping behaviorin the member of the population cohort based on an association betweenthe population cohort and the at least one cohort-linked avatarcomprises: circuitry for indicating at least one behavior in the memberof the population cohort based on a statistical association between thepopulation cohort and the at least one cohort-linked avatar.
 32. Thesystem of claim 17 wherein the circuitry for identifying a member of apopulation cohort comprises: circuitry for indicating at least onebehavior in the member of the population cohort based on an associationbetween the population cohort and at least one avatar linked to themember of the population cohort.
 33. A computer program productcomprising: a computer-readable medium bearing (a) one or moreinstructions for identifying a member of a population cohort; and (b)one or more instructions for indicating at least one behavior in themember of the population cohort based on an association between thepopulation cohort and at least one cohort-linked avatar, where the atleast one cohort-linked avatar is at least partly based on a mentalstate associated with at least one physiologic activity measurement, andwhere at least one of a weight or a priority is assigned to at least oneof a reference mental state or at least one reference physiologicactivity measurement.
 34. The computer program product of claim 33,wherein the computer-readable medium includes a recordable medium. 35.The computer program product of claim 33, wherein the computer-readablemedium includes a communications medium.
 36. A system comprising: acomputing device; and instructions that when executed on the computingdevice cause the computing device to (a) identify a member of apopulation cohort; and (b) predict at least one behavior of the memberof the population cohort based on an association between the behaviorand at least one cohort-linked avatar, where the at least onecohort-linked avatar is at least partly based on a mental stateassociated with at least one physiologic activity measurement, and whereat least one of a weight or a priority is assigned to at least one of areference mental state or at least one reference physiologic activitymeasurement.
 37. The system of claim 36 wherein the computing devicecomprises: one or more of a personal digital assistant (PDA), a personalentertainment device, a mobile phone, a laptop computer, a tabletpersonal computer, a networked computer, a computing system comprised ofa cluster of processors, a computing system comprised of a cluster ofservers, a workstation computer, and/or a desktop computer.
 38. Thesystem of claim 36 wherein the computing device is operable to identifya member of a population cohort from at least one memory.
 39. The systemof claim 36 wherein the computing device is operable to predict from atleast one memory at least one behavior of the member of the populationcohort based on an association between the behavior and at least onecohort-linked avatar.